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

TL;DR

This study uses machine learning to detect early signs of metabolic syndrome in military personnel, showing better accuracy with sex-specific models.

Contribution

The novelty lies in developing and validating sex-specific machine learning models for early metabolic syndrome detection in military populations.

Findings

Logistic regression achieved the highest accuracy (0.89) for predicting metabolic syndrome.

Sex-specific models identified distinct risk factors, such as total cholesterol in men and hemoglobin in women.

Key predictors like BMI, age, and alanine aminotransferase levels were consistent across both sexes.

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.

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