Mental Health Monitoring Dashboard with Predictive Analytics

Abstract

Mental health problems such as stress, anxiety, and sleep disorders are increasing rapidly due to modern lifestyle, academic pressure, and work-related stress. Early identification a continuous monitoring of mental health conditions are essential to prevent severe psychological issues. However, traditional mental health assessment methods are mostly manual, time-consuming, and depend on physical consultations, which limits regular monitoring and early detection. This project proposes a Mental Health Monitoring Dashboard with Predictive Analytics, a web-based system designed to monitor, analyze, and predict mental health risks effectively. The system collects user-reported data such as mood, stress levels, and sleep patterns through a user-friendly interface. The collected data is processed and analyzed using data analytics and predictive techniques to identify potential mental health risks at an early stage. The backend of the system is developed using Python and Flask, while the frontend is implemented using HTML, CSS, and JavaScript. An interactive dashboard visually presents insights, trends, and risk levels, making the results easy to understand for users. This system helps users to become aware of their mental health status and supports preventive care through early alerts and data-driven insights. Overall, the proposed system provides an efficient, accessible, and scalable solution for continuous mental health monitoring and early risk prediction.