Prediction and predictor elucidation of metabolic syndrome onset among young workers using machine learning techniques: A nationwide study in Japan

Abstract

Objectives: Predictive models for the onset of metabolic syndrome (MS) for people in their 30s are scarce. This study aimed to construct a highly accurate model to predict MS onset by 40 years of age and to identify important predictors of MS onset using health checkup data of Japanese employees aged between 30 and 35 years. Methods: The study included 6,048 Japanese employees aged 40 years who underwent periodic health examinations over 10 years. We developed predictive models for MS onset using machine learning methods, including random forest and logistic regression models. The variable importance of each explanatory variable was calculated to identify important predictors of MS onset for the random forest models. Results: Of 2,998 participants, 164 participants aged 30 and 180 of 4,045 participants aged 35 years developed MS by age 40 years. The random forest models have the highest predictive power (e.g., AU-ROC 0.867 for males aged 30) compared to the logistic regression models. In these models, diastolic blood pressure was the most important predictor of MS onset for males, while body mass index was the most important predictor for females. Conclusions: We created machine learning models to predict MS onset at the age of 40 years with high accuracy from health examination data obtained at the age of 30 or 35 years. Sex differences in important predictors of MS onset were shown by the variable importance indices of the random forest. Applying our model in routine healthcare management could provide early health interventions to prevent MS onset.

Keywords

• blood pressure
• body mass index
• machine learning
• metabolic syndrome
• obesity
• risk