Application of machine learning in predicting health perception through military personnel's sense of empowerment.

Abstract

The promotion of health and provision of care services for new recruits are issues of constant concern for military leaders and healthcare providers, as they are crucial to maintaining and operating military forces. The enhancement of military personnel's empowerment has been recognized as a core value in promoting health perception. However, the pathways between military personnel's sense of empowerment and health perception have not been thoroughly explored. The primary aim of this study is to examine the predictive power of different dimensions of empowerment (personal, interpersonal, and socio-political) on new recruits' health perception, and to further observe differences among subgroups, which will help us grasp the nuances of future health intervention measures. The research data were extracted from the "Military Career Development Study," analyzing personal empowerment data from Wave 1 (W1) and perceived health data from Wave 2 (W2) (N = 2,232). In terms of analytical methods, five ML classifiers, including Decision Tree, Random Forest, Support Vector Machine, AdaBoost, and k-Nearest Neighbors (KNN) algorithms, were used for prediction in both the full sample and subsamples (gender and socioeconomic status). Results show that among the five ML classifiers, the Decision Tree performed best overall, achieving a prediction accuracy of 95.4%. The results by gender show that the ML models perform best for both males and females with the Decision Tree and Random Forest methods. For the Decision Tree, the accuracy rates were 94.9% for males and 95.1% for females; the F1 scores were 92.9% for males and 93.2% for females. For the Random Forest, the accuracy rates were 94.9% for males and 95.4% for females; the F1 scores were 92.7% for males and 93.2% for females. Regarding SES, the Decision Tree and Random Forest methods performed best. In the SES Low group, both methods achieved a prediction accuracy of 95.6% and an F1 score of 93.7%; in the SES high group, they achieved a prediction accuracy of 95.4% and an F1 score of 93.3%. However, the contribution of different dimensions of empowerment features varied significantly among subgroups. These findings can provide important information on the differences in health perception among military personnel.