Prediction of Cardiovascular Disease by Applying Feature Extraction

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Disease by Applying Feature Extraction</title> <meta name="description" content="Prediction of Cardiovascular Disease by Applying Feature Extraction"> <meta name="keywords" content="Cardiovascular disease, feature extraction, supervised learning, k-NN."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Prediction of Cardiovascular Disease by Applying Feature Extraction"> <meta name="citation_author" content="Nebi Gedik"> <meta name="citation_publication_date" content="2024/04/02"> <meta name="citation_journal_title" content="International Journal of Biomedical and Biological Engineering"> <meta name="citation_volume" content="18"> <meta name="citation_issue" content="4"> <meta name="citation_firstpage" content="75"> <meta name="citation_lastpage" content="80"> <meta name="citation_pdf_url" content="https://publications.waset.org/10013584/pdf"> <link 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Heart issues lead to many of all deaths; therefore, early diagnosis and treatment are critical. The diagnosis of heart disease is complicated due to several factors affecting health such as high blood pressure, raised cholesterol, an irregular pulse rhythm, and more. Artificial intelligence has the potential to assist in the early detection and treatment of diseases. Improving heart failure prediction is one of the primary goals of research on heart disease risk assessment. This study aims to determine the features that provide the most successful classification prediction in detecting cardiovascular disease. The performances of each feature are compared using the K-Nearest Neighbor machine learning method. The feature that gives the most successful performance has been identified.</p> <iframe src="https://publications.waset.org/10013584.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cardiovascular%20disease" title="Cardiovascular disease">Cardiovascular disease</a>, <a href="https://publications.waset.org/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/search?q=k-NN." title=" k-NN."> k-NN.</a> </p> <a href="https://publications.waset.org/10013584/prediction-of-cardiovascular-disease-by-applying-feature-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013584/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013584/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013584/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013584/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013584/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013584/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013584/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013584/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013584/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013584/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013584.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] National Institutes of Health, Practical Guide: Identification, Evaluation and Treatment of Overweight and Obesity in Adults, National Institutes of Health0, New York, NY, U.S.A, 2000. <br>[2] Who Health Organization (WHO), cardiovascular diseases. https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 <br>[3] D. Deng, P. Jiao, X. Ye, and L. Xia, “An image-based model of the whole human heart with detailed anatomical structure and fiber orientation,” Computational and Mathematical Methods in Medicine, vol. 2012, 2012, p.16. <br>[4] A. Ishaq, S. Sadiq, M. Umer et al., “Improving the prediction of heart failure patients’ survival using SMOTE and effective data mining techniques,” IEEE Access, vol. 9, 2021, pp. 39707–39716. <br>[5] C. J. Harrison and C. J. S. Gibbons, “Machine learning in medicine: a practical introduction to natural language processing,” BMC Medical Research Methodology, vol. 21(1), 2021, p. 158. <br>[6] İ. Ozcan, B. Tasar, A. B. Tatar, and O. Yakut, “Destek Vektör Makinesi Algoritması ile Kalp Hastalıklarının Tahmini”, Computer Science, 4(2), 2019, pp. 74- 79. <br>[7] P. Rani, R. Kumar, N. M. Ahmed, and A. Jain, “A decision support system for heart disease prediction based upon machine learning,” Journal of Reliable Intelligent Environments, vol. 7, 2021. <br>[8] M. G. Feshki and O. S. Shijani, “Improving the heart disease diagnosis by evolutionary algorithm of PSO and feed forward neural network,” in Proceedings of the 2016 Artificial Intelligence and Robotics (IRANOPEN), IEEE, Qazvin, Iran, April 2016, pp. 48–53. <br>[9] S. Mohan, C. 'irumalai, and G. Srivastava, “Effective heart disease prediction using hybrid machine learning techniques,” IEEE Access, vol. 7, 2019, pp. 81542–81554. <br>[10] Xu, Shan, et al. "Cardiovascular risk prediction method based on CFS subset evaluation and random forest classification framework." 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA). IEEE, 2017. <br>[11] A. Saboor, M. Usman, S. Ali, A. Samad, M. F. Abrar, andN. Ullah, “A method for improving prediction of human heart disease using machine learning algorithms,” Mob. Inf.Syst., 2022, pp. 1–9. <br>[12] G. N. Ahmad et al., “Mixed machine learning approach for efficient prediction of human heart disease by identifying the numerical and categorical features,” Appl. Sci. (Basel), vol. 12, no. 15, 2022, p. 7449. <br>[13] N. Absar et al., “The efficacy of machine-learning-supported smart system for heart disease prediction,” Healthcare (Basel), vol. 10, no. 6, 2022, p. 1137. <br>[14] K. Kira, L.A. Rendell, “The feature selection problem: traditional methods and a new algorithm”, in: AAAI, vol. 2, 1992a, pp. 129–134. <br>[15] K. Kira, L.A. Rendell, “A practical approach to feature selection”, in: Proceedings of the Ninth International Workshop on Machine Learning, 1992b, pp. 249–256. <br>[16] J.P. Callan, T. Fawcett, E.L. Rissland, “Cabot: an adaptive approach to case-based search”, in: IJCAI, vol. 12, 1991, pp. 803–808. <br>[17] D.W. Aha, D. Kibler, M.K. Albert, “Instance-based learning algorithms”, Mach. Learn. Vol. 6 (1), 1991, pp. 37–66. <br>[18] S.F. Rosario, and K. Thangadurai. "RELIEF: feature selection approach." International journal of innovative research and development, vol. 4(11), 2015, pp. 2018-224. <br>[19] L.E. Peterson. "K-nearest neighbor." Scholarpedia, vol. 4.2, 2009. p. 1883. <br>[20] Rényi, Alfréd. "On measures of entropy and information." Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1: Contributions to the Theory of Statistics. Vol. 4. University of California Press, 1961. <br>[21] Mucherino, Antonio, et al. "K-nearest neighbor classification." Data mining in agriculture (2009): 83-106. <br>[22] John, Peter WM. "The analysis of variance." Modern Statistics, Methods and Applications, vol. 23, 1980, p. 19. <br>[23] CVD dataset https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

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Prediction of Cardiovascular Disease by Applying Feature Extraction