Machine learning-aided risk prediction for metabolic syndrome based on 3 years study

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding risk factors. In this study, we investigate the risk prediction for MetS using a data set of 67,730 samples with physical examination records of three consecutive years provided by the Department of Health Management, Nanfang Hospital, Southern Medical University, P.R. China. Specifically, the prediction for MetS takes the numerical features of examination records as well as the differential features by using the examination records over the past two consecutive years, namely, the differential numerical feature (DNF) and the differential state feature (DSF), and the risk factors of the above features w.r.t different ages and genders are statistically analyzed. From numerical results, it is shown that the proposed DSF in addition to the numerical feature of examination records, significantly contributes to the risk prediction of MetS. Additionally, the proposed scheme, by using the proposed features, yields a superior performance to the state-of-the-art MetS prediction model, which provides the potential of effective prescreening the occurrence of MetS.
Funding Information
  • Science and Technology Program of Guangzhou (No. 202002030353, No.2019050001)
  • Science and Technology Planning Project of Guangdong Province (No. 2019A1515011940)
  • Industry-Academia-Research Innovation Project of Blue-Fire of Ministry of Education (No. CXZJHZ201803)

This publication has 38 references indexed in Scilit: