Hyunseok Shin, Simon Shim, Sejong Oh.
Abstract
Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios.
Introduction
Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, hyperglycemia, and dyslipidemia. Although there are slight differences in the details, there are five common risk factors: fasting plasma glucose, blood pressure, triglycerides, high-density lipoprotein cholesterol, and waist circumference. MetS is diagnosed if more than three factors among these are abnormal.
Materials and Methods:
Procedure
Depicts the process used to develop our MetS machine learning predictive model. We used the health checkup records to develop the model. These records contained the core elements of anthropometry and blood test results that could identify MetS. The data included survey results on lifestyle, diet, family history, and medical history.
Discussion:
We developed a predictive model for MetS that utilizes only noninvasive information, making it practical for use in real-world scenarios. While fasting blood sugar, triglycerides, and HDL cholesterol are important factors in diagnosing MetS, we deliberately excluded features that require blood testing when developing our predictive model, to ensure its preventive usability.
Citation: Shin H, Shim S, Oh S (2023) Machine learning-based predictive model for prevention of metabolic syndrome. PLoS ONE 18(6): e0286635. https://doi.org/10.1371/journal.pone.0286635
Editor: Shakuntala Baichoo, University of Mauritius, MAURITIUS
Received: January 3, 2023; Accepted: May 19, 2023; Published: June 2, 2023.
Copyright: © 2023 Shin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly because of ethical restrictions. Data are available from the Korea Disease Control and Prevention Agency's Institutional Data Access / Ethics Committee (contact via division of Population Health Research [http://www.cdc.go.kr]) for researchers who meet the criteria for access to confidential data.
Funding: This study was supported by the Ministry of Science, ICT (MSIT), Korea, under the High-Potential Individuals Global Training Program (2021-0-01531) and the R&D program of Development of AI ophthalmologic diagnosis and smart treatment platform based on big data(2018–0-00242) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
