Smart Depression Detection: A KNN-Based Multimodal Approach for Stress Level Classification

Mental health issues like stress and sadness are becoming more common in today's fast-paced, extremely demanding society. Early detection and intervention are therefore essential to stop these conditions from deteriorating. Using data gathered from the Smart_Pillow_Stress_Detection_Machine_Learning dataset, this project offers a smart mobile app that uses machine learning algorithms to forecast user stress and depression levels. Given its simplicity and efficacy in managing health-related behavioral data, the program uses the K-Nearest Neighbors (KNN) classification approach. The system uses sleep patterns, heart rate, and other vital signs recorded via a smart pillow to examine physiological and behavioral parameters in order to categorize the user's mental state into different degrees of stress and sadness. Supported healthier and more balanced lives, this smart prediction tool seeks to help people monitor their mental health and act timely.