Predicting Mental Health Outcomes Using Wearable Device Data and Machine Learning

This paper proposes a machine learning-based system designed to predict mental health outcomes using wearable device data. The system is conceptualized to process physiological and behavioral data such as heart rate, sleep patterns, and activity levels collected from wearable technology. Key stages of the system include data preprocessing, feature extraction, and model training using multiple machine-learning algorithms, including Random Forest, Support Vector Machine, XGBoost, and Logistic Regression. These models are combined using a voting-based ensemble classifier to improve prediction accuracy. While the system has not yet been implemented, expected results suggest that this approach will enhance prediction reliability and offer real-time insights into mental health conditions. The proposed system is envisioned to facilitate early detection of mental health disorders, thereby aiding in timely interventions and personalized care.