Early risk detection of metabolic syndrome using sex-specific machine learning models in military personnel

Abstract

Metabolic syndrome is a critical predictor of future cardiometabolic disease and an emerging public health concern, particularly in high-demand populations such as military personnel. This study aimed to develop and evaluate sex-specific machine learning models for the early detection of metabolic syndrome using annual health check data. We analyzed records from 179,620 Taiwanese Air Force personnel between 2014 and 2022, incorporating demographic, anthropometric, clinical, lifestyle, mental health, and biochemical variables. Six machine learning algorithms—including logistic regression, random forest, K-nearest neighbor, support vector machine, neural network, and naïve Bayes—were trained separately for men and women. Among these models, logistic regression outperformed the others, achieving an accuracy and area under the curve (AUC) of 0.89. Body mass index, age, and alanine aminotransferase levels were consistent predictors across sexes. For men, total cholesterol and uric acid contributed significantly, while hemoglobin and hematocrit were more predictive in women. These findings demonstrate that sex-specific predictive models can support early identification of individuals at high risk for metabolic syndrome, enabling targeted prevention strategies and strengthening population health efforts in military populations and other young to middle-aged adult groups.