Metabolic syndrome predictive modelling in Bangladesh applying machine learning approach.

Autor: Hossain MF; Division of Computing, Analytics and Mathematics, Department of Mathematics and Statistics, School of Science and Engineering, University of Missouri, Kansas City, MO, United States of America.; Department of Statistics, Comilla University, Cumilla, Bangladesh., Hossain S; Department of Statistics, Comilla University, Cumilla, Bangladesh., Akter MN; Department of Statistics, Comilla University, Cumilla, Bangladesh., Nahar A; Department of Statistics, Comilla University, Cumilla, Bangladesh., Liu B; Division of Computing, Analytics and Mathematics, Department of Mathematics and Statistics, School of Science and Engineering, University of Missouri, Kansas City, MO, United States of America., Faruque MO; Division of Energy, Matter and Sciences, School of Science and Engineering, University of Missouri, Kansas City, MO, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Sep 05; Vol. 19 (9), pp. e0309869. Date of Electronic Publication: 2024 Sep 05 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0309869
Abstrakt: Metabolic syndrome (MetS) is a cluster of interconnected metabolic risk factors, including abdominal obesity, high blood pressure, and elevated fasting blood glucose levels, that result in an increased risk of heart disease and stroke. In this research, we aim to identify the risk factors that have an impact on MetS in the Bangladeshi population. Subsequently, we intend to construct predictive machine learning (ML) models and ultimately, assess the accuracy and reliability of these models. In this particular study, we utilized the ATP III criteria as the basis for evaluating various health parameters from a dataset comprising 8185 participants in Bangladesh. After employing multiple ML algorithms, we identified that 27.8% of the population exhibited a prevalence of MetS. The prevalence of MetS was higher among females, accounting for 58.3% of the cases, compared to males with a prevalence of 41.7%. Initially, we identified the crucial variables using Chi-Square and Random Forest techniques. Subsequently, the obtained optimal variables are employed to train various models including Decision Trees, Random Forests, Support Vector Machines, Extreme Gradient Boosting, K-nearest neighbors, and Logistic Regression. Particularly we employed the ATP III criteria, which utilizes the Waist-to-Height Ratio (WHtR) as an anthropometric index for diagnosing abdominal obesity. Our analysis indicated that Age, SBP, WHtR, FBG, WC, DBP, marital status, HC, TGs, and smoking emerged as the most significant factors when using Chi-Square and Random Forest analyses. However, further investigation is necessary to evaluate its precision as a classification tool and to improve the accuracy of all classifiers for MetS prediction.
Competing Interests: There is no conflict of interest among the authors.
(Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje