{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T18:58:23Z","timestamp":1760986703717},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,29]]},"abstract":"<jats:p>Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). However, developing and under-developing countries are still struggling to build EHR in their hospitals. Due to the lack of HER data, early screening tools are not available for those countries. This study develops a prediction model for early DM by direct questionnaires for a tertiary hospital in Bangladesh. Information gain technique was used to reduce irreverent features. Using selected variables, we developed logistic regression, support vector machine, K-nearest neighbor, Na\u00efve Bayes, random forest (RF), and neural network models to predict diabetes at an early stage. RF outperformed other machine learning algorithms achieved 100% accuracy. These findings suggest that a combination of simple questionnaires and a machine learning algorithm can be a powerful tool to identify undiagnosed DM patients.<\/jats:p>","DOI":"10.3233\/shti220752","type":"book-chapter","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T07:35:15Z","timestamp":1656574515000},"source":"Crossref","is-referenced-by-count":5,"title":["Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques"],"prefix":"10.3233","author":[{"given":"Tahmina Nasrin","family":"Poly","sequence":"first","affiliation":[{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan"},{"name":"International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan"},{"name":"Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan"}]},{"given":"Md Mohaimenul","family":"Islam","sequence":"additional","affiliation":[{"name":"AESOP Technology, Songshan District, Taipei 105, Taiwan"},{"name":"International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan"},{"name":"Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan"}]},{"given":"Yu-Chuan (Jack)","family":"Li","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan"},{"name":"AESOP Technology, Songshan District, Taipei 105, Taiwan"},{"name":"International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan"},{"name":"Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan"},{"name":"Department of Dermatology, Wan Fang Hospital, Taipei 116, Taiwan"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Advances in Informatics, Management and Technology in Healthcare"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI220752","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T07:35:16Z","timestamp":1656574516000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI220752"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti220752","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]}}}