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<!DOCTYPE html> <html> <head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <title>Prediction of Anemia using Machine Learning Algorithms</title>  <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <meta name="title" content="Prediction of Anemia using Machine Learning Algorithms"> <meta name="description" content="Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream. This research aims to design a model for prediction of Anemia in children under 5 years of age using Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were applied. It is followed by verification, validation along with result analysis. Random Forest is the best performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms. Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5 years of age." /> <meta name="keywords" content="Machine learning, Anemia, Children, Prediction, Algorithm, Accuracy"/>   <meta name="dc.title" content="Prediction of Anemia using Machine Learning Algorithms "> <meta name="citation_authors" content="Prakriti Dhakal"> <meta name="citation_authors" content="Santosh Khanal"> <meta name="citation_authors" content="Rabindra Bista"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="International Journal of Computer Science & Information Technology (IJCSIT) Vol 15, No 1"> <meta name="dc.date" content="2022/02/28"> <meta name="dc.identifier" content="10.5121/ijcsit.2023.15102"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream. This research aims to design a model for prediction of Anemia in children under 5 years of age using Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were applied. It is followed by verification, validation along with result analysis. Random Forest is the best performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms. Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5 years of age. "/> <meta name="dc.subject" content="Machine learning"> <meta name="dc.subject" content=" Anemia"> <meta name="dc.subject" content=" Children"> <meta name="dc.subject" content=" Prediction"> <meta name="dc.subject" content=" Algorithm"> <meta name="dc.subject" content=" Accuracy">   <meta name="prism.publicationName" content="International Journal of Computer Science & Information Technology (IJCSIT) "> <meta name="prism.publicationDate" content="2022/02/28"> <meta name="prism.volume" content="15"> <meta name="prism.number" content="1"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="15">   <meta name="citation_journal_title" content="International Journal of Computer Science & Information Technology (IJCSIT)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_authors" content="Prakriti Dhakal, Santosh Khanal, and Rabindra Bista "> <meta name="citation_title" content="Prediction of Anemia using Machine Learning Algorithms"> <meta name="citation_online_date" content="2022/02/28"> <meta name="citation_issue" content="15"> <meta name="citation_firstpage" content="15"> <meta name="citation_authors" content="Prakriti Dhakal"> <meta name="citation_authors" content="Santosh Khanal"> <meta name="citation_authors" content="Rabindra Bista"> <meta name="citation_doi" content="10.5121/ijcsit.2023.15102"> <meta name="citation_abstract_html_url" content="https://aircconline.com/abstract/ijcsit/v15n1/15123ijcsit02.html"> <meta name="citation_pdf_url" content="https://aircconline.com/ijcsit/V15N1/15123ijcsit02.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/abstract/ijcsit/v15n1/15123ijcsit02.html"> <meta property="og:title" content="Prediction of Anemia using Machine Learning Algorithms "> <meta property="og:description" content="Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream. This research aims to design a model for prediction of Anemia in children under 5 years of age using Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were applied. It is followed by verification, validation along with result analysis. Random Forest is the best performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms. Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5 years of age. "/>   <meta name="google-site-verification" content="t8rHIcM8EfjIqfQzQ0IdYIiA9JxDD0uUZAitBCzsOIw" /> <meta name="yandex-verification" content="e3d2d5a32c7241f4" />  <style type="text/css"> a{ color:white; text-decoration:none; } ul li a{ font-weight:bold; color:#000; list-style:none; text-decoration:none; size:10px;} .imagess { height:90px; text-align:left; margin:0px 5px 2px 8px; float:right; border:none; } #left p { font-size:0.90pc; margin-left: 20px; } .right { margin-right: 20px; } #button{ float: left; font-size: 17px; margin-left: 10px; height: 28px; width: 100px; background-color: #1e86c6; } </style> <link rel="icon" type="image/ico" href="/abstract/ico.ico"/> <link rel="stylesheet" type="text/css" href="../main.css" /> </head> <body> <div id="wap"> <div id="page"> <div id="top"> <table width="100%" cellspacing="0" cellpadding="0" > <tr><td colspan="3" valign="top"><img src="../top3.gif" /></td></tr> </table> </div> <div id="menu"> <a href="http://airccse.org/journal/ijcsit.html">Home</a> <a href="http://airccse.org/journal/eboard.html">Editorial</a> <a href="http://airccse.org/journal/papersub.html">Submission</a> <a href="http://airccse.org/journal/jcsit_index.html">Indexing</a> <a href="http://airccse.org/journal/specialissue.html">Special Issue</a> <a href="http://airccse.org/journal/contacts.html">Contacts</a> <a href="http://airccse.org/" target="blank">AIRCC</a></div> <div id="content"> <div id="left"> <h2>Volume 15, Number 1</h2> <h4 style="text-align:center;height:auto"><a>Prediction of Anemia using Machine Learning Algorithms</a></h4> <h3>  Authors</h3> <p class="#left">Prakriti Dhakal, Santosh Khanal, and Rabindra Bista, Kathmandu University, Nepal </p> <h3>  Abstract</h3> <p class="#left right" style="text-align:justify">Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream. This research aims to design a model for prediction of Anemia in children under 5 years of age using Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were applied. It is followed by verification, validation along with result analysis. Random Forest is the best performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms. Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5 years of age. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Machine learning, Anemia, Children, Prediction, Algorithm, Accuracy.</p><br> <button type="button" id="button"><a target="blank" href="/ijcsit/V15N1/15123ijcsit02.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://airccse.org/journal/ijcsit2023_curr.html">Volume 15</a></button> <br><br><br><br><br> </div> </div> <div id="right"> <div class="menu_right"> <ul><li><a href="http://airccse.org/journal/ijcsit.html">Scope & Topics</a></li> <li><a href="http://airccse.org/ethics.html" target="_blank">Ethics</a></li> <li><a href="http://airccse.org/journal/csit_archives.html">Archives</a></li> <li><a href="http://airccse.org/journal/papersub.html">Paper Submission</a></li> <li><a href="http://airccse.org/journal/ijcsit_cited.html">Most Cited Articles</a></li> <li><a href="http://airccse.org/journal/IJCSITleaflet.pdf">Download leaflet</a></li> <li><a href="http://airccse.org/faq.html" target="_blank">FAQ</a></li> </ul> </div><br /> <p align="center"> </p> <p align="center"> </p> </div> <div class="clear"></div> <div id="footer"><table width="100%" ><tr><td height="25" colspan="2"><br /><p align="center">© AIRCC Publishing Corporation</p></td></table> </div> </div> </div> </body> </html>