Integrated Prediction of Mental Health and Fitness Using Machine Learning Models

This study investigates the development of a machine learning model, specifically utilizing the Random Forest algorithm, to predict an individual's mental health conditions and fitness levels. By integrating physiological data (heart rate, sleep patterns, physical activity) with behavioral data (stress, coping mechanisms), the model provides a comprehensive assessment of individual wellbeing. Data sourced from wearables, GitHub repositories, and Kaggle mental health surveys are used to train and refine the model. The model's performance is evaluated using metrics such as accuracy, cross-validation, and precision, ensuring robustness and reliability. Feature importance analysis identifies key factors influencing mental and physical health, enabling the development of targeted interventions and personalized healthcare strategies. This research contributes to the advancement of personalized healthcare by providing valuable insights into individual well-being and enabling proactive approaches to mental and physical health management.