Risk prediction of chronic diseases with a two-stage semi-supervised clustering method.

ABSTRACT: Early detection of chronic diseases such as cardiovascular disease (CVD) and diabetes can make the difference between life and death. Previous studies have demonstrated the feasibility of disease diagnosis and prediction using machine learning and disease-indicating biomarkers. The aim of this study is to develop a method to detect the risk of future disease even when disease-indicating biomarker readings are in the normal range. Data from the US Centers for Disease Control and Prevention (CDC) National Health and Nutrition Examination Surveys (NHANES) are used for this study. A two-stage semi-supervised K-Means (SSK-Means) clustering approach was developed to identify the underlying risk of each individual and categorize them into high or low-risk groups for CVD and diabetes. Our develope

SUBMITTER: Mao Z 

PROVIDER: S-EPMC9929447 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature