A Machine Learning Classification Model for Detecting Prediabetes
A Machine Learning Classification Model for Detecting Prediabetes作者机构：Department of Mathematics & Computer Science Augustana College Rock Island Illinois USA Independent Researcher San Francisco California USA Department of Mathematics & Statistics University of South Florida Tampa Florida USA
出 版 物：《Journal of Data Analysis and Information Processing》 (数据分析和信息处理（英文）)
年 卷 期：2024年第12卷第3期
页 面：462-478页
学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学]
主 题：Prediabetes Machine Learning SVM Forest Cumulative Lift
摘 要：The incidence of prediabetes is in a dangerous condition in the USA. The likelihood of increasing chronic and complex health issues is very high if this stage of prediabetes is ignored. So, early detection of prediabetes conditions is critical to decrease or avoid type 2 diabetes and other health issues that come as a result of untreated and undiagnosed prediabetes condition. This study is done in order to detect the prediabetes condition with an artificial intelligence method. Data used for this study is collected from the Centers for Disease Control and Prevention’s (CDC) survey conducted by the Division of Health and Nutrition Examination Surveys (DHANES). In this study, several machine learning algorithms are exploited and compared to determine the best algorithm based on Average Squared Error (ASE), Kolmogorov-Smirnov (Youden) scores, areas under the ROC and some other measures of the machine learning algorithm. Based on these scores, the champion model is selected, and Random Forest is the champion model with approximately 89% accuracy.
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