Gender Differences in Predicting Metabolic Syndrome Among Hospital Employees Using Machine Learning Models: A Population-Based Study.

Background: Metabolic syndrome (MetS) is a complex condition that captures several markers of dysregulation, including obesity, elevated blood glucose levels, dyslipidemia and hypertension. Using an approach to early prediction of MetS risk in hospital employees that takes into account the differing effects of gender may be expected to improve cardiovascular disease-related health outcomes.

Purpose: In this study, machine learning techniques were applied to construct an optimized MetS prediction model for use on hospital employees.

Methods: This population-based study survey included 3,537 participants aged 20 to 65 years old. Participant demographic, anthropometric data, medical history, lifestyle-related factor, and biochemical data were collected from the hospital's Health Management Information System from 2018 to 2020. MetS prediction and the investigation of gender differences were performed using six machine learning models based on the following algorithms: K-nearest neighbor, random forest, logistic regression, support vector machine, neural network, and Naïve Bayes. All analyses were performed by sequentially inputting the features in three steps according to their characteristics.

Results: MetS was detected in 8.91% of the participants. Among the MetS prediction models, Naïve Bayes showed the best performance, with a sensitivity of 0.825, an accuracy of 0.859 and an area under the receiver operating characteristic curve of 0.936. Body mass index and alanine transaminase were identified as important predictive factors for MetS in participants of both genders. Age, uric acid, and aspartate transaminase were identified as important predictive factors in men, while chronic disease and phosphorous were identified as important predictive factors in women.

Conclusions: The results indicate Naïve Bayes model to be useful and accurate in identifying MetS in hospital employees independent of gender. The early prediction of MetS using a model that accounts for gender differences is an important part of routine health screening and requires a multidimensional approach, including self-administered questionnaires and anthropometric and biochemical measurements.