Abstract
The health monitoring of military personnel is critical for sustaining operational readiness and minimizing medical risks. In this study, we present an artificial intelligence-driven application that classifies the health status of service members based on two simple medical parameters: heart rate and body weight. The system integrates two complementary approaches: supervised learning using decision trees and unsupervised learning with the K-Means algorithm. Experimental evaluation demonstrates that the decision tree achieved an accuracy of 90%, as confirmed by the confusion matrix, while the K-Means method identified distinct clusters aligned with clinically relevant health profiles: healthy, cardiac risk, and metabolic risk. These results highlight the potential of artificial intelligence to be effectively integrated into military medical monitoring systems, supporting early diagnosis and enhancing the efficiency of health assessment processes.