Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease

<!DOCTYPE html>    <html>  <head> <meta charset="utf-8" /> <title>Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease</title>  <link rel="shortcut icon" type="image/ico" href="./data/ijsom/coversheet/favicon.ico" />  <meta name="viewport" content="width=device-width, maximum-scale=1, initial-scale=1, user-scalable=0" />   <meta name="keywords" content="Keywords: Diabetes Mellitus,Machine Learning Algorithm,Data mining,Accuracy,Area under Curve,Multi-Verse Optimizer,Multi-Layer Perceptron" /> <meta name="description" content="Diabetes Mellitus is one of the most chronic diseases in all over the world. Every year, many people die due to this disease in all countries. Therefore, identifying early detection methods for this disease can reduce its mortality. Today, many diseases can be diagnosed and prevented from progressing by using data mining techniques and machine learning algorithms. In this paper, diabetes prediction has been aimed by comparing the efficiency of several classical machine-learning techniques. For this reason, for the sake of diabetes prediction algorithms such as Na茂ve Bayes, Logistic Regression (LR), Multi-Layer Perceptron (MLP), Sequential Minimal Optimization (SMO), J48, Random Forest (RF), Regression Tree (RT) algorithms and a new hybrid algorithm based on Multi-Verse Optimizer (MVO) and Multi-Layer Perceptron (MLP) algorithms are employed for this evaluation based on Accuracy (ACC) Indicator and Area under Curve (AUC) criteria. Numerous and diverse methods and algorithms have been used to predict diabetes. Each of these algorithms has been effective in predicting diabetes with a different level of accuracy. Our goal in this research is to introduce a new combined algorithm that has the highest level of accuracy in predicting diabetes compared to the old frequent algorithms so that it can help people in the timely treatment of this disease. In the structure of the MLP algorithm, the backpropagation algorithm is used for training. This article uses the MVO algorithm to train the MLP instead of the backpropagation algorithm, which built the hybrid algorithm called MVO-MLP. The accuracy results and the area under the ROC diagram Indicated that the proposed hybrid algorithm increases the accuracy by 107% compared to the MLP algorithm with the default structure. The outcomes of the accuracy of the new model are also higher than other algorithms used in this article" /> <meta name="title" content="Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease" /> <meta name="googlebot" content="NOODP" /> <meta name="citation_title" content="Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease" /> <meta name="citation_author" content="Nodoust, Azin" /> <meta name="citation_author_institution" content="Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran" /> <meta name="citation_author" content="Rajabzadeh Ghatari, Ali" /> <meta name="citation_author_institution" content="Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran" /> <meta name="citation_abstract" content="Diabetes Mellitus is one of the most chronic diseases in all over the world. Every year, many people die due to this disease in all countries. Therefore, identifying early detection methods for this disease can reduce its mortality. Today, many diseases can be diagnosed and prevented from progressing by using data mining techniques and machine learning algorithms. In this paper, diabetes prediction has been aimed by comparing the efficiency of several classical machine-learning techniques. For this reason, for the sake of diabetes prediction algorithms such as Na茂ve Bayes, Logistic Regression (LR), Multi-Layer Perceptron (MLP), Sequential Minimal Optimization (SMO), J48, Random Forest (RF), Regression Tree (RT) algorithms and a new hybrid algorithm based on Multi-Verse Optimizer (MVO) and Multi-Layer Perceptron (MLP) algorithms are employed for this evaluation based on Accuracy (ACC) Indicator and Area under Curve (AUC) criteria. Numerous and diverse methods and algorithms have been used to predict diabetes. Each of these algorithms has been effective in predicting diabetes with a different level of accuracy. Our goal in this research is to introduce a new combined algorithm that has the highest level of accuracy in predicting diabetes compared to the old frequent algorithms so that it can help people in the timely treatment of this disease. In the structure of the MLP algorithm, the backpropagation algorithm is used for training. This article uses the MVO algorithm to train the MLP instead of the backpropagation algorithm, which built the hybrid algorithm called MVO-MLP. The accuracy results and the area under the ROC diagram Indicated that the proposed hybrid algorithm increases the accuracy by 107% compared to the MLP algorithm with the default structure. The outcomes of the accuracy of the new model are also higher than other algorithms used in this article" /> <meta name="citation_id" content="2945" /> <meta name="citation_publication_date" content="2024/11/01" /> <meta name="citation_date" content="2024-11-01" /> <meta name="citation_journal_title" content="International Journal of Supply and Operations Management" /> <meta name="citation_issn" content="23831359" /> <meta name="citation_volume" content="11" /> <meta name="citation_issue" content="4" /> <meta name="citation_firstpage" content="462" /> <meta name="citation_lastpage" content="482" /> <meta name="citation_publisher" content="Kharazmi University" /> <meta name="citation_doi" content="10.22034/ijsom.2024.110346.3067" /> <meta name="DC.Identifier" content="10.22034/ijsom.2024.110346.3067" /> <meta name="citation_abstract_html_url" content="http://www.ijsom.com/article_2945.html" /> <meta name="citation_pdf_url" content="http://www.ijsom.com/article_2945_74b94fa84c5fa3710592d5934571a596.pdf" /> <meta name="DC.Title" content="Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease" /> <meta name="DC.Source" content="International Journal of Supply and Operations Management" /> <meta name="DC.Date" content="01/11/2024" /> <meta name="DC.Date.issued" content="2024-11-01" /> <meta name="DC.Format" content="application/pdf" /> <meta name="DC.Contributor" content="Nodoust, Azin" /> <meta name="DC.Contributor" content="Rajabzadeh Ghatari, Ali" /> <meta name="og:title" content="Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease" /> <meta name="og:description" content="Diabetes Mellitus is one of the most chronic diseases in all over the world. Every year, many people die due to this disease in all countries. Therefore, identifying early detection methods for this disease can reduce its mortality. Today, many diseases can be diagnosed and prevented from progressing by using data mining techniques and machine learning algorithms. In this paper, diabetes prediction has been aimed by comparing the efficiency of several classical machine-learning techniques. For this reason, for the sake of diabetes prediction algorithms such as Na茂ve Bayes, Logistic Regression (LR), Multi-Layer Perceptron (MLP), Sequential Minimal Optimization (SMO), J48, Random Forest (RF), Regression Tree (RT) algorithms and a new hybrid algorithm based on Multi-Verse Optimizer (MVO) and Multi-Layer Perceptron (MLP) algorithms are employed for this evaluation based on Accuracy (ACC) Indicator and Area under Curve (AUC) criteria. Numerous and diverse methods and algorithms have been used to predict diabetes. Each of these algorithms has been effective in predicting diabetes with a different level of accuracy. Our goal in this research is to introduce a new combined algorithm that has the highest level of accuracy in predicting diabetes compared to the old frequent algorithms so that it can help people in the timely treatment of this disease. In the structure of the MLP algorithm, the backpropagation algorithm is used for training. This article uses the MVO algorithm to train the MLP instead of the backpropagation algorithm, which built the hybrid algorithm called MVO-MLP. The accuracy results and the area under the ROC diagram Indicated that the proposed hybrid algorithm increases the accuracy by 107% compared to the MLP algorithm with the default structure. The outcomes of the accuracy of the new model are also higher than other algorithms used in this article" /> <meta name="og:url" content="http://www.ijsom.com/article_2945.html" />  <link href="./themes/base/front/assets/css/social-icon-font.css" rel="stylesheet" type="text/css" />  <link href="./themes/base/front/assets/plugins/bootstrap/css/bootstrap.min.css?v=0.02" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/header.css?v=0.05" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/footer.css" rel="stylesheet" type="text/css" /> <link href="./inc/css/essentials.css?v=0.2" rel="stylesheet" type="text/css" /> <link href="./inc/css/cookieconsent.min.css" rel="stylesheet" type="text/css" /> <link href="./inc/css/print.css" rel="stylesheet" type="text/css" media="print"/>  <link href="./themes/base/front/assets/plugins/bootstrap/css/bootstrap-ltr.min.css" rel="stylesheet" type="text/css" /> <link href=" ./themes/base/front/assets/css/gfonts-OpenSans.css" rel="stylesheet" type="text/css" /> <link href="./themes/old/front/assets/css/accordian.css" rel="stylesheet" type="text/css" /> <link href="./themes/base/front/assets/css/academicons.min.css" rel="stylesheet" type="text/css" />  <meta name="google-site-verification" content="UlbWpwabckk_wTgRmzoJCVPEYKnomtCLcftujpXdou4" /> <link href="./data/ijsom/coversheet/stl_front.css?v=0.42" rel="stylesheet" type="text/css" /> <link href="./data/ijsom/coversheet/stl.css" rel="stylesheet" type="text/css" />  <link rel="alternate" type="application/rss+xml" title="RSS feed" href="ju.rss" /> <script type="text/javascript" src="./themes/base/front/assets/plugins/jquery/jquery.min.js?v=0.5"></script> <script type="text/javascript" src="./inc/js/common.js?v=0.1"></script> <script type="text/javascript" src="./inc/js/jquery/cookieconsent.min.js"></script>   <script src="inc/js/article.js?v=0.31" type="text/javascript" ></script> </head> <body class="ltr len"> <div class="container" id="header"> <div class="row"> <div class="col-xs-12 text-center"> <img src="./data/ijsom/coversheet/head_en.jpg" class="img-responsive text-center" style="display:-webkit-inline-box; width: 100%;" > </div> </div> </div> <div class="container"> <div class="row"> <div class="col-xs-12 col-lg-12 col-md-12 text-center"> <nav class="navbar navbar-default noborder nomargin noradius" role="navigation"> <div class="container-fluid nopadding" > <div class="navbar-header" style="background: #FFFFFF;"> <button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar"></span> <span class="icon-bar"></span> <span class="icon-bar"></span> </button>  </div>  <div class="collapse navbar-collapse nopadding" id="bs-example-navbar-collapse-1"> <ul class="nav navbar-nav"> <li><a href="././"> Home</a></li> <li class="dropdown"> <a href="" class="dropdown-toggle" data-toggle="dropdown">Browse <b class="caret"></b></a> <ul class="dropdown-menu"> <li><a href="./?_action=current">Current Issue</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./browse?_action=issue">By Issue</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./browse?_action=author">By Author</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./browse?_action=subject">By Subject</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./author.index">Author Index</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./keyword.index">Keyword Index</a></li> </ul> </li> <li class="dropdown"> <a href="" class="dropdown-toggle" data-toggle="dropdown">Journal Info <b class="caret"></b></a> <ul class="dropdown-menu"> <li><a href="./journal/about">About Journal</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./journal/aim_scope">Aims and Scope</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./journal/editorial.board">Editorial Board</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./journal/process?ethics">Publication Ethics</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./journal/indexing">Indexing and Abstracting</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./journal/links">Related Links</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./journal/faq">FAQ</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./journal/process">Peer Review Process</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./journal/metrics">Journal Metrics</a></li> <li class="divider margin-bottom-6 margin-top-6"></li> <li><a href="./news">News</a></li> </ul> </li> <li><a href="./journal/authors.note"> Guide for Authors</a></li> <li><a href="./author"> Submit Manuscript</a></li> <li><a href="./reviewer?_action=info"> Reviewers</a></li> <li><a href="./journal/contact.us"> Contact Us</a></li> </ul> <ul class="nav navbar-nav navbar-right nomargin"> <li><a href="./contacts">Login</a></li> <li><a href="./contacts?_action=signup">Register</a></li> </ul> </div>  </div>  </nav> </div> </div> </div>  <div class="container" > <div id="dv_main_cnt"> <section class="no-cover-box"> <div class="row">  <div class="col-lg-9 col-md-9 col-sm-8" id="dv_artcl">  <div> <h1 class="margin-bottom-20 size-18 ltr"><span class="article_title bold"> Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease</span></h1> <div> <div class="margin-bottom-3"> </div> <p class="margin-bottom-3">Document Type : Research Paper</p> <p class="padding-0" style="margin:12px -2px 0 -2px"><strong>Authors</strong></p> <ul class="list-inline list-inline-seprator margin-bottom-6 ltr"> <li class="padding-3"> <a href="./?_action=article&au=131119&_au=Azin++Nodoust">Azin Nodoust</a> <sup><a href="mailto:n.azin@modares.ac.ir" data-toggle="tooltip" data-placement="bottom" title="Email to Corresponding Author"><i class="fa fa-envelope-o" ></i></a></sup> </li> <li class="padding-3"> <a href="./?_action=article&au=118659&_au=Ali++Rajabzadeh+Ghatari">Ali Rajabzadeh Ghatari</a> </li> </ul> <p class="margin-bottom-3 ltr" id="aff1"> Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran </p> <div class="margin-bottom-3 ltr" id="ar_doi" title="DOI"><i class="ai ai-doi size-25 text-orange"></i> <span dir="ltr"><a href="https://dx.doi.org/10.22034/ijsom.2024.110346.3067">10.22034/ijsom.2024.110346.3067</a></span></div> <p style="margin:12px -2px 0 -2px"><strong>Abstract</strong></p> <div class="padding_abstract justify ltr">Diabetes Mellitus is one of the most chronic diseases in all over the world. Every year, many people die due to this disease in all countries. Therefore, identifying early detection methods for this disease can reduce its mortality. Today, many diseases can be diagnosed and prevented from progressing by using data mining techniques and machine learning algorithms. In this paper, diabetes prediction has been aimed by comparing the efficiency of several classical machine-learning techniques. For this reason, for the sake of diabetes prediction algorithms such as Naïve Bayes, Logistic Regression (LR), Multi-Layer Perceptron (MLP), Sequential Minimal Optimization (SMO), J48, Random Forest (RF), Regression Tree (RT) algorithms and a new hybrid algorithm based on Multi-Verse Optimizer (MVO) and Multi-Layer Perceptron (MLP) algorithms are employed for this evaluation based on Accuracy (ACC) Indicator and Area under Curve (AUC) criteria. Numerous and diverse methods and algorithms have been used to predict diabetes. Each of these algorithms has been effective in predicting diabetes with a different level of accuracy. Our goal in this research is to introduce a new combined algorithm that has the highest level of accuracy in predicting diabetes compared to the old frequent algorithms so that it can help people in the timely treatment of this disease. In the structure of the MLP algorithm, the backpropagation algorithm is used for training. This article uses the MVO algorithm to train the MLP instead of the backpropagation algorithm, which built the hybrid algorithm called MVO-MLP. The accuracy results and the area under the ROC diagram Indicated that the proposed hybrid algorithm increases the accuracy by 107% compared to the MLP algorithm with the default structure. The outcomes of the accuracy of the new model are also higher than other algorithms used in this article</div> <p class="padding-0" style="margin:12px -2px 0 -2px"><strong>Keywords</strong></p> <ul class="block list-inline list-inline-seprator margin-bottom-6 ltr"> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106548&_kw=Keywords%3A+Diabetes+Mellitus" >Keywords: Diabetes Mellitus</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106549&_kw=Machine+Learning+Algorithm" >Machine Learning Algorithm</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106183&_kw=Data+Mining" >Data Mining</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106550&_kw=Accuracy" >Accuracy</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106551&_kw=Area+under+Curve" >Area under Curve</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106552&_kw=Multi-Verse+Optimizer" >Multi-Verse Optimizer</a> </li> <li class="padding-3"> <a class="tag_a" href="./?_action=article&kw=106553&_kw=Multi-Layer+Perceptron" >Multi-Layer Perceptron</a> </li> </ul> </div> <hr> <div class="page_break"></div> <div class="panel"> <div class="panel-heading card-header"> <h4 class="panel-title "> <a data-toggle="collapse" data-parent="#accordions" href="#collapsesRef"><i class="fa fa-plus"></i> References</a> </h4> </div> <div id="collapsesRef" class="panel-collapse collapse"> <div class="panel-body justify"> <div class="padding-3 margin-top-3 ltr justify">Ahmad, A., Mustapha, A., Zahadi, E. D., Masah, N., & Yahaya, N. Y. (2011). Comparison between neural networks against decision tree in improving prediction accuracy for diabetes mellitus. In <em>Digital Information Processing and Communications: International Conference, ICDIPC 2011, Ostrava, Czech Republic, July 7-9, 2011, Proceedings, Part I</em> (pp. 537-545). Springer Berlin Heidelberg.Ahmad, F., et al., <em>Intelligent medical disease diagnosis using improved hybrid genetic algorithm-multilayer perceptron network.</em> Journal of Medical Systems, 2013. <strong>37</strong>(2): p. 1-8. </div> <div class="padding-3 margin-top-3 ltr justify">Ahmed, T. M. (2016). Using data mining to develop model for classifying diabetic patient control level based on historical medical records. <em>Journal of Theoretical and Applied Information Technology</em>, <em>87</em>(2), 316. </div> <div class="padding-3 margin-top-3 ltr justify">Ahmed, U., Issa, G. F., Khan, M. A., Aftab, S., Khan, M. F., Said, R. A., ... & Ahmad, M. (2022). Prediction of diabetes empowered with fused machine learning. <em>IEEE Access</em>, <em>10</em>, 8529-8538.Alam, T.M., et al., <em>A model for early prediction of diabetes.</em> Informatics in Medicine Unlocked, 2019. <strong>16</strong>: p. 100204. </div> <div class="padding-3 margin-top-3 ltr justify">Bellamy, L., Casas, J. P., Hingorani, A. D., & Williams, D. (2009). Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. <em>The lancet</em>, <em>373</em>(9677), 1773-1779. </div> <div class="padding-3 margin-top-3 ltr justify">Butwall, M., & Kumar, S. (2015). A data mining approach for the diagnosis of diabetes mellitus using random forest classifier. <em>International Journal of Computer Applications</em>, <em>120</em>(8). </div> <div class="padding-3 margin-top-3 ltr justify">Chang, V., Bailey, J., Xu, Q. A., & Sun, Z. (2023). Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms. <em>Neural Computing and Applications</em>, <em>35</em>(22), 16157-16173. </div> <div class="padding-3 margin-top-3 ltr justify">Chang, V., Ganatra, M. A., Hall, K., Golightly, L., & Xu, Q. A. (2022). An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators. <em>Healthcare Analytics</em>, <em>2</em>, 100118. </div> <div class="padding-3 margin-top-3 ltr justify">Cox, M. E., & Edelman, D. (2009). Tests for screening and diagnosis of type 2 diabetes. <em>Clinical diabetes</em>, <em>27</em>(4), 132-138. </div> <div class="padding-3 margin-top-3 ltr justify">Dharmarathne, G., Jayasinghe, T. N., Bogahawaththa, M., Meddage, D. P. P., & Rathnayake, U. (2024). A novel machine learning approach for diagnosing diabetes with a self-explainable interface. <em>Healthcare Analytics</em>, <em>5</em>, 100301. </div> <div class="padding-3 margin-top-3 ltr justify">Fatima, M., & Pasha, M. (2017). Survey of machine learning algorithms for disease diagnostic. <em>Journal of Intelligent Learning Systems and Applications</em>, <em>9</em>(01), 1-16. </div> <div class="padding-3 margin-top-3 ltr justify">Ganie, S. M., & Malik, M. B. (2022). An ensemble machine learning approach for predicting type-II diabetes mellitus based on lifestyle indicators. <em>Healthcare Analytics</em>, <em>2</em>, 100092. </div> <div class="padding-3 margin-top-3 ltr justify">Goyal, M., Malik, R., Kumar, D., Rathore, S., & Arora, R. (2020). Musculoskeletal abnormality detection in medical imaging using GnCNNr (group normalized convolutional neural networks with regularization). <em>SN Computer Science</em>, <em>1</em>(6), 1-12. </div> <div class="padding-3 margin-top-3 ltr justify">Gowthami, S., Reddy, R. V. S., & Ahmed, M. R. (2024). Exploring the effectiveness of machine learning algorithms for early detection of Type-2 Diabetes Mellitus. <em>Measurement: Sensors</em>, <em>31</em>, 100983. </div> <div class="padding-3 margin-top-3 ltr justify"> Himsworth, H. P., & Kerr, R. B. (1939). Insulin-sensitive and insulin-insensitive types of diabetes mellitus. </div> <div class="padding-3 margin-top-3 ltr justify">Hina, S., Shaikh, A., & Sattar, S. A. (2017). Analyzing diabetes datasets using data mining. Journal of Basic & Applied Sciences, 13, 466-471. </div> <div class="padding-3 margin-top-3 ltr justify">Islam, M. M., Rahman, M. J., Roy, D. C., & Maniruzzaman, M. (2020). Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(3), 217-219.. </div> <div class="padding-3 margin-top-3 ltr justify">Iyer, A., Jeyalatha, S., & Sumbaly, R. (2015). Diagnosis of diabetes using classification mining techniques. arXiv preprint arXiv:1502.03774. </div> <div class="padding-3 margin-top-3 ltr justify">Kalyankar, G. D., Poojara, S. R., & Dharwadkar, N. V. (2017, February). Predictive analysis of diabetic patient data using machine learning and Hadoop. In 2017 international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC) (pp. 619-624). IEEE. </div> <div class="padding-3 margin-top-3 ltr justify">Kangra, K., & Singh, J. (2023). Comparative analysis of predictive machine learning algorithms for diabetes mellitus. Bulletin of Electrical Engineering and Informatics, 12(3), 1728-1737. </div> <div class="padding-3 margin-top-3 ltr justify">Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine learning and data mining methods in diabetes research. Computational and structural biotechnology journal, 15, 104-116. </div> <div class="padding-3 margin-top-3 ltr justify">Khaleel, F. A., & Al-Bakry, A. M. (2023). Diagnosis of diabetes using machine learning algorithms. <em>Materials Today: Proceedings</em>, <em>80</em>, 3200-3203. </div> <div class="padding-3 margin-top-3 ltr justify">Khan, D. M., & Mohamudally, N. (2011). An integration of K-means and decision tree (ID3) towards a more efficient data mining algorithm. <em>Journal of Computing</em>, <em>3</em>(12), 76-82. </div> <div class="padding-3 margin-top-3 ltr justify">Krishnamoorthi, R., Joshi, S., Almarzouki, H. Z., Shukla, P. K., Rizwan, A., Kalpana, C., & Tiwari, B. (2022). [Retracted] A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques. <em>Journal of healthcare engineering</em>, <em>2022</em>(1), 1684017. </div> <div class="padding-3 margin-top-3 ltr justify">Kumari, S., Kumar, D., & Mittal, M. (2021). An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier. <em>International Journal of Cognitive Computing in Engineering</em>, <em>2</em>, 40-46. </div> <div class="padding-3 margin-top-3 ltr justify">Shaw, J. E., Sicree, R. A., & Zimmet, P. Z. (2010). Global estimates of the prevalence of diabetes for 2010 and 2030. <em>Diabetes research and clinical practice</em>, <em>87</em>(1), 4-14. </div> <div class="padding-3 margin-top-3 ltr justify">Shetty, D., Rit, K., Shaikh, S., & Patil, N. (2017, March). Diabetes disease prediction using data mining. In <em>2017 international conference on innovations in information, embedded and communication systems (ICIIECS)</em> (pp. 1-5). IEEE. </div> <div class="padding-3 margin-top-3 ltr justify">Lu, H., Uddin, S., Hajati, F., Moni, M. A., & Khushi, M. (2022). A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus. <em>Applied Intelligence</em>, <em>52</em>(3), 2411-2422. </div> <div class="padding-3 margin-top-3 ltr justify">Lukmanto, R. B., Nugroho, A., & Akbar, H. (2019). Early detection of diabetes mellitus using feature selection and fuzzy support vector machine. <em>Procedia Computer Science</em>, <em>157</em>, 46-54. </div> <div class="padding-3 margin-top-3 ltr justify">Marcano-Cedeño, A., Torres, J., & Andina, D. (2011, May). A prediction model to diabetes using artificial metaplasticity. In <em>International Work-Conference on the Interplay Between Natural and Artificial Computation</em> (pp. 418-425). Berlin, Heidelberg: Springer Berlin Heidelberg. </div> <div class="padding-3 margin-top-3 ltr justify">El Massari, H., Sabouri, Z., Mhammedi, S., & Gherabi, N. (2022). Diabetes prediction using machine learning algorithms and ontology. <em>Journal of ICT Standardization</em>, <em>10</em>(2), 319-337. </div> <div class="padding-3 margin-top-3 ltr justify">Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. <em>Neural Computing and Applications</em>, <em>27</em>, 495-513. </div> <div class="padding-3 margin-top-3 ltr justify">Nithya, B., & Ilango, V. (2017, June). Predictive analytics in health care using machine learning tools and techniques. In <em>2017 International Conference on Intelligent Computing and Control Systems (ICICCS)</em> (pp. 492-499). IEEE. </div> <div class="padding-3 margin-top-3 ltr justify">Olokoba, A. B., Obateru, O. A., & Olokoba, L. B. (2012). Type 2 diabetes mellitus: a review of current trends. <em>Oman medical journal</em>, <em>27</em>(4), 269. </div> <div class="padding-3 margin-top-3 ltr justify">Patil, B. M., Joshi, R. C., & Toshniwal, D. (2010). Hybrid prediction model for type-2 diabetic patients. <em>Expert systems with applications</em>, <em>37</em>(12), 8102-8108. </div> <div class="padding-3 margin-top-3 ltr justify">Polat, K., & Güne艧, S. (2007). An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. <em>Digital signal processing</em>, <em>17</em>(4), 702-710. </div> <div class="padding-3 margin-top-3 ltr justify">Rawat, V., Joshi, S., Gupta, S., Singh, D. P., & Singh, N. (2022). Machine learning algorithms for early diagnosis of diabetes mellitus: A comparative study. <em>Materials Today: Proceedings</em>, <em>56</em>, 502-506. </div> <div class="padding-3 margin-top-3 ltr justify">Samsel, K., Tiwana, A., Ali, S., Sadeghi, A., Guergachi, A., Keshavjee, K., ... & Shakeri, Z. (2024). Predicting depression among canadians at-risk or living with diabetes using machine learning. <em>medRxiv</em>, 2024-02. </div> <div class="padding-3 margin-top-3 ltr justify">Theerthagiri, P., Ruby, A. U., & Vidya, J. (2022). Diagnosis and classification of the diabetes using machine learning algorithms. <em>SN Computer Science</em>, <em>4</em>(1), 72. </div> <div class="padding-3 margin-top-3 ltr justify"> Wilson, R. A., & Keil, F. C. (1999). The MIT encyclopedia of the cognitive sciences. A Bradford book.. </div> <div class="padding-3 margin-top-3 ltr justify">Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., & Tang, H. (2018). Predicting diabetes mellitus with machine learning techniques. <em>Frontiers in genetics</em>, <em>9</em>, 515.</div> </div> </div> </div> </div> </div>   <div class="col-lg-3 col-md-3 col-sm-4"> <div class="panel panel-default my_panel-default margin-bottom-10"> <div class="panel-body ar_info_pnl" id="ar_info_pnl_cover"> <div id="pnl_cover"> <div class="row" > <div class="col-xs-6 col-md-6 nomargin-bottom"> <a href="javascript:loadModal('International Journal of Supply and Operations Management', './data/ijsom/coversheet/cover_en.jpg')"> <img src="data/ijsom/coversheet/cover_en.jpg" alt="International Journal of Supply and Operations Management" style="width: 100%;"> </a> </div> <div class="col-xs-6 col-md-6 nomargin-bottom"> <h6><a href="./issue_542_546.html">Volume 11, Issue 4</a><br/>November 2024<div id="sp_ar_pages">Pages <span dir="ltr">462-482</span></div></h6> </div> </div> </div> </div> </div>  <div class="panel panel-default my_panel-default margin-bottom-10 panel-lists"> <div class="panel-heading"> <h3 class="panel-title"><a data-toggle="collapse" data-parent="#accordion" href="#ar_info_pnl_fl"><i class="fa fa-files-o"></i> Files</a></h3> </div> <div id="ar_info_pnl_fl" class="panel-collapse collapse in"> <div class="panel-body ar_info_pnl padding-6"> <ul class="list-group list-group-bordered list-group-noicon nomargin"> <li class="list-group-item"><a href="./?_action=xml&article=2945" target="_blank" class="tag_a pdf_link"><i class="fa fa-file-code-o text-orange" ></i> XML</a></li> <li class="list-group-item"><a href="./article_2945_74b94fa84c5fa3710592d5934571a596.pdf" target="_blank" class="tag_a pdf_link"><i class="fa fa-file-pdf-o text-red" ></i> PDF 1.48 MB</a></li>  </ul> </div> </div> </div> <div class="panel panel-default my_panel-default margin-bottom-10"> <div class="panel-heading"> <h3 class="panel-title"><a data-toggle="collapse" data-parent="#accordion" href="#ar_info_pnl_share"><i class="fa fa-share-square-o" aria-hidden="true"></i> Share</a></h3> </div> <div id="ar_info_pnl_share" class="panel-collapse collapse"> <div class="panel-body ar_info_pnl padding-10 text-center"> <a id="share_facebook" href="https://www.facebook.com/sharer.php?u=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-facebook" data-toggle="tooltip" data-placement="top" title="Facebook"> <i class="icon-facebook"></i> <i class="icon-facebook"></i> </a> <a id="share_linkedin" href="https://www.linkedin.com/shareArticle?mini=true&url=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-linkedin" data-toggle="tooltip" data-placement="top" title="Linkedin"> <i class="icon-linkedin"></i> <i class="icon-linkedin"></i> </a> <a id="share_mendeley" href="https://www.mendeley.com/import/?url=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-youtube" data-toggle="tooltip" data-placement="top" title="Mendeley"> <i class="icon-mendeley"></i> <i class="icon-mendeley"></i> </a> <a id="share_refworks" href="https://www.refworks.com/express/ExpressImport.asp?url=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-disqus" data-toggle="tooltip" data-placement="top" title="Refworks"> <i class="icon-refworks"><span class="path1"></span><span class="path2"></span><span class="path3"></span><span class="path4"></span><span class="path5"></span><span class="path6"></span><span class="path7"></span><span class="path8"></span><span class="path9"></span><span class="path10"></span></i> <i class="icon-refworks"><span class="path1"></span><span class="path2"></span><span class="path3"></span><span class="path4"></span><span class="path5"></span><span class="path6"></span><span class="path7"></span><span class="path8"></span><span class="path9"></span><span class="path10"></span></i> </a> <a id="share_instagram" href="https://www.instagram.com/?url=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-instagram" data-toggle="tooltip" data-placement="top" title="Instagram"> <i class="icon-instagram"></i> <i class="icon-instagram"></i> </a> <a id="share_twitter" href="https://twitter.com/share?url=http://www.ijsom.com/article_2945.html&text=Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease" target="_blank" class="social-icon social-icon-sm social-twitter" data-toggle="tooltip" data-placement="top" title="Twitter"> <i class="icon-twitter"></i> <i class="icon-twitter"></i> </a> <a id="share_email" href="javascript:act('email')" class="social-icon social-icon-sm social-email3 " data-toggle="tooltip" data-placement="top" title="Email"> <i class="icon-email3"></i> <i class="icon-email3"></i> </a> <a id="share_print" href="javascript:printDiv('dv_artcl')" class="social-icon social-icon-sm social-print" data-toggle="tooltip" data-placement="top" title="Print"> <i class="icon-print"></i> <i class="icon-print"></i> </a> <a id="share_stumble" href="https://mix.com/mixit?su=submit&url=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-stumbleupon" data-toggle="tooltip" data-placement="top" title="StumbleUpon"> <i class="icon-stumbleupon"></i> <i class="icon-stumbleupon"></i> </a> <a id="share_acedemia" href="https://www.academia.edu/" target="_blank" class="social-icon social-icon-sm social-academia" data-toggle="tooltip" data-placement="top" title="Academia"> <i class="ai ai-academia"></i> <i class="ai ai-academia"></i> </a> <a id="share_sems" href="https://www.semanticscholar.org/" target="_blank" class="social-icon social-icon-sm social-forrst" data-toggle="tooltip" data-placement="top" title="Semantic scholar"> <i class="ai ai-semantic-scholar"></i> <i class="ai ai-semantic-scholar"></i> </a> <a id="share_reddit" href="https://www.reddit.com/submit?url=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-dwolla" data-toggle="tooltip" data-placement="top" title="Reddit"> <i class="icon-reddit"></i> <i class="icon-reddit"></i> </a> <a id="share_rg" href="https://www.researchgate.net/" target="_blank" class="social-icon social-icon-sm social-researchgate" data-toggle="tooltip" data-placement="top" title="Research Gate"> <i class="ai ai-researchgate"></i> <i class="ai ai-researchgate"></i> </a> <a id="share_blogger" href="https://www.blogger.com/blog-this.g?u=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-blogger" data-toggle="tooltip" data-placement="top" title="Blogger"> <i class="icon-blogger"></i> <i class="icon-blogger"></i> </a> <a id="share_pinterest" href="https://pinterest.com/pin/create/bookmarklet/?media=&url=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-pinterest" data-toggle="tooltip" data-placement="top" title="Pinterest"> <i class="icon-pinterest"></i> <i class="icon-pinterest"></i> </a> <a id="share_digg" href="https://www.digg.com/submit?http://www.ijsom.com/article_2945.html&title=Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease" target="_blank" class="social-icon social-icon-sm social-digg" data-toggle="tooltip" data-placement="top" title="Digg"> <i class="icon-digg"></i> <i class="icon-digg"></i> </a> <a id="share_delicious" href="https://del.icio.us/post?url=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-delicious" data-toggle="tooltip" data-placement="top" title="Delicious"> <i class="icon-delicious"></i> <i class="icon-delicious"></i> </a> <a id="share_skype" href="https://web.skype.com/share?url=http://www.ijsom.com/article_2945.html" target="_blank" class="social-icon social-icon-sm social-skype" data-toggle="tooltip" data-placement="top" title="Skype"> <i class="icon-skype"></i> <i class="icon-skype"></i> </a> </div> </div> </div>  <div class="panel panel-default my_panel-default margin-bottom-10 panel-lists"> <div class="panel-heading"> <h3 class="panel-title"><a data-toggle="collapse" data-parent="#accordion" href="#ar_info_pnl_cite"><i class=" fa fa-external-link"></i> How to cite</a></h3> </div> <div id="ar_info_pnl_cite" class="panel-collapse collapse "> <div class="panel-body ar_info_pnl"> <ul class="list-group list-group-bordered list-group-noicon" style="display:block !important;max-height:9999px"> <li class="list-group-item ltr"><a class="tag_a" href="./?_action=export&rf=ris&rc=2945">RIS</a></li> <li class="list-group-item ltr"><a class="tag_a" href="./?_action=export&rf=enw&rc=2945">EndNote</a></li> <li class="list-group-item ltr"><a class="tag_a" href="./?_action=export&rf=ris&rc=2945">Mendeley</a></li> <li class="list-group-item ltr"><a class="tag_a" href="./?_action=export&rf=bibtex&rc=2945">BibTeX</a></li> <li class="list-group-item ltr"><a class="tag_a" href="javascript:void(0)" data-toggle="modal" data-target="#cite-apa">APA</a></li> <li class="list-group-item ltr"><a class="tag_a" href="javascript:void(0)" data-toggle="modal" data-target="#cite-mla">MLA</a></li> <li class="list-group-item ltr"><a class="tag_a" href="javascript:void(0)" data-toggle="modal" data-target="#cite-harvard">HARVARD</a></li> <li class="list-group-item ltr"><a class="tag_a" href="javascript:void(0)" data-toggle="modal" data-target="#cite-vancouver">VANCOUVER</a></li> </ul> </div> </div> </div>  <div class="panel panel-default my_panel-default panel-lists"> <div class="panel-heading"> <h3 class="panel-title"><a data-toggle="collapse" data-parent="#accordion" href="#ar_info_pnl_st"><i class="fa fa-bar-chart" aria-hidden="true"></i> Statistics</a></h3> </div> <div id="ar_info_pnl_st" class="panel-collapse collapse in"> <div class="panel-body ar_info_pnl"> <ul class="list-group list-group-bordered list-group-noicon" style="display:block !important;max-height:9999px"> <li class="list-group-item"><a class="tag_a">Article View: <i>178</i></a></li> <li class="list-group-item"><a class="tag_a">PDF Download: <i>111</i></a></li> </ul> </div> </div> </div> </div>  </div> </section> <div id="cite-apa" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">APA</h4> </div>  <div class="modal-body"> <p>Nodoust, A., & Rajabzadeh Ghatari, A. (2024). Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease. <em>International Journal of Supply and Operations Management</em>, 11(4), 462-482. doi: 10.22034/ijsom.2024.110346.3067</p> </div> </div> </div> </div> <div id="cite-mla" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">MLA</h4> </div>  <div class="modal-body"> <p>Azin Nodoust; Ali Rajabzadeh Ghatari. "Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease". <em>International Journal of Supply and Operations Management</em>, 11, 4, 2024, 462-482. doi: 10.22034/ijsom.2024.110346.3067</p> </div> </div> </div> </div> <div id="cite-harvard" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">HARVARD</h4> </div>  <div class="modal-body"> <p>Nodoust, A., Rajabzadeh Ghatari, A. (2024). 'Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease', <em>International Journal of Supply and Operations Management</em>, 11(4), pp. 462-482. doi: 10.22034/ijsom.2024.110346.3067</p> </div> </div> </div> </div> <div id="cite-vancouver" class="modal fade" tabindex="-1" role="dialog" aria-labelledby="myModalLabel" aria-hidden="true"> <div class="modal-dialog"> <div class="modal-content">  <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">VANCOUVER</h4> </div>  <div class="modal-body"> <p>Nodoust, A., Rajabzadeh Ghatari, A. Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease. <em>International Journal of Supply and Operations Management</em>, 2024; 11(4): 462-482. doi: 10.22034/ijsom.2024.110346.3067</p> </div> </div> </div> </div> </div> </div>   <section class="alternate padding-xxs"> </section>   <div class="container"> <footer id="footer"> <div class="scrollup" id="scroll" href="#"><span></span></div> <div class="row"> <div class="col-md-2">  <h4 class="">Explore Journal</h4> <ul class="footer-links list-unstyled"> <li id="fli_home"><a href="./">Home</a></li> <li id="fli_about"><a href="./journal/about">About Journal</a></li> <li id="fli_Edb"><a href="./journal/editorial.board">Editorial Board</a></li> <li id="fli_submit"><a href="./author">Submit Manuscript</a></li> <li id="fli_contactus"><a href="./journal/contact.us">Contact Us</a></li> <li id="fli_glossary"><a href="./journal/glossary">Glossary</a></li> <li id="fli_order_hrdj"><a href="./journal/subscription.form">Hard Copy Subscription</a></li> <li id="fli_sitemap"><a href="./sitemap.xml?usr">Sitemap</a></li> </ul>  </div> <div class="col-md-3">  <h4 class="">Latest News</h4> <ul class="footer-posts list-unstyled"> <li> <a href="./news?newsCode=173">SD of ISC: Sustainable Development of Intelligent Supply Chains based on Trends and Future Directions: Application of Novel Solution Techniques</a> <small class="ltr">2023-03-05</small> </li> </ul>  </div> <div class="col-md-3">  <div><p><a title="Linkedin" href="http://www.linkedin.com/company/ijsom?trk=eml-cp_mktg-btn-welcome-20120607%2F"><img src="images/linkedin.jpg" alt="linkedin" /></a></p></div>  </div> <div class="col-md-4">  <h4 class="">Newsletter Subscription</h4> <p>Subscribe to the journal newsletter and receive the latest news and updates</p> <form class="validate" action="" method="post" data-success="Subscription saved successfully." data-toastr-position="bottom-right"> <input type="hidden" name="_token" value="6abb5ee245fb5e617fff9aec86eb5b47700303c1478cbd1b"/> <div class="input-group"> <span class="input-group-addon"><i class="fa fa-envelope"></i></span> <input type="email" id="email" name="email" required="required" class="form-control required sbs_email" placeholder="Enter your Email" oninvalid="this.setCustomValidity('Enter a valid email address.')" oninput="this.setCustomValidity('')"> <span class="input-group-btn"> <button class="btn btn-primary mybtn" type="submit">Subscribe</button> </span> </div> </form>   <div class="margin-top-20"> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Facebook"> <i class="fa fa-facebook-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Twitter"> <i class="fa fa-twitter-square" aria-hidden="true"></i> </a> <a class="noborder" href="" target="_blank" class="social-icon social-icon-border social-facebook pull-left block" data-toggle="tooltip" data-placement="top" title="Linkedin"> <i class="fa fa-linkedin-square" aria-hidden="true"></i> </a> <a class="noborder" href="./ju.rss" class="social-icon social-icon-border social-rss pull-left block" data-toggle="tooltip" data-placement="top" title="Rss"><i class="fa fa-rss-square" aria-hidden="true"></i></a> </div> </div> </div> <div class="copyright" style="position: relative"> <ul class="nomargin list-inline mobile-block"> <li>© Journal Management System. <span id='sp_crt'>Powered by <a target='_blank' href='https://www.sinaweb.net/'>Sinaweb</a></span></li> </ul> </div> </footer> </div>  </div>   <a href="#" id="toTop_old"></a>  <div id="preloader"> <div class="inner"> <span class="loader"></span> </div> </div>  <script type="text/javascript">var plugin_path = './themes/base/front/assets/plugins/';</script> <script type="text/javascript" src="./themes/base/front/assets/js/scripts.js?v=0.02"></script>   <script type="text/javascript"> $('ul.nav li.dropdown').hover(function() { if (window.matchMedia('(max-width: 767px)').matches) return; $(this).find('.dropdown-menu').stop(true, true).delay(200).fadeIn(500); }, function() { if (window.matchMedia('(max-width: 767px)').matches) return; $(this).find('.dropdown-menu').stop(true, true).delay(200).fadeOut(500); }); var btn = $('#toTop_old'); $(window).scroll(function() { if ($(window).scrollTop() > 300) { btn.addClass('show'); } else { btn.removeClass('show'); } }); btn.on('click', function(e) { e.preventDefault(); $('html, body').animate({scrollTop:0}, '300'); }); window.cookieconsent.initialise({ "palette": { "popup": { "background": "#222" }, "button": { "background": "#f1d600" } }, "content": { "message": "This website uses cookies to ensure you get the best experience on our website.", "dismiss": "Got it!", "link": "" } }); </script> </body> </html><div id="actn_modal" class="modal fade" tabindex="-1"> <div id="" class="modal-dialog modal-dialog madal-aw"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close pull-right" data-dismiss="modal" aria-hidden="true" href="#lost">×</button> <h5 class="modal-title"></h5> </div> <div class="modal-body"></div> <div class="modal-footer"></div> </div> </div> </div>

CINXE.COM

Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease