amber-system

byNikhath MF

Build a web-based system called **MindCare AI**, which performs mental health risk assessment and provides basic guidance to users. The goal is not medical diagnosis but awareness and early intervention. Start by designing the system as a structured pipeline. The user should first interact with a questionnaire-based interface. This questionnaire must follow a standardized scale (0–3 rating per question) and should cover key indicators such as mood, sleep, energy, and concentration. The input must be clean, validated, and consistent. Once the user submits responses, the backend should process the data in two ways. First, implement a rule-based scoring system that calculates the total score and maps it into risk categories such as low, moderate, or high. Second, integrate a machine learning model (preferably SVM or Logistic Regression) that takes the same inputs and predicts a risk level. Combine both outputs using a safe decision rule (for example, selecting the higher severity level) to avoid underestimating risk. After determining the final risk category, pass the result to a recommendation engine. This component should generate structured output based on severity. For low risk, provide general wellness tips like maintaining routine and physical activity. For moderate risk, include coping strategies such as journaling, meditation, or talking to trusted individuals. For high risk, the system must strongly recommend seeking professional help and display a clear warning message. In parallel, implement a mood tracking feature. Users should be able to log their daily emotional state. Store this data in a lightweight database such as SQLite. This data will later be used to build a dashboard where users can view their mood history over time. If possible, include a simple visualization like a trend graph to enhance usability and presentation quality. From a frontend perspective, the application must include the following pages: a landing page with a clear purpose and navigation, an assessment page with structured questions, a results page showing risk level and recommendations, and a dashboard page displaying stored mood data. The UI should be clean, minimal, and consistent, using calm colors and readable typography. There are several mandatory requirements that must not be skipped. The system must include a disclaimer clearly stating that it is not a medical diagnostic tool. Input validation must be enforced to prevent invalid or missing data. The system should avoid collecting personally identifiable information to maintain basic privacy standards. Additionally, the logic must always prioritize user safety by not underestimating risk levels. The final system should demonstrate a hybrid approach combining rule-based logic and machine learning, persistent data handling through a database, and a user-friendly interface that supports both assessment and tracking. The focus should be on clarity, usability, and responsible output rather than claiming high medical accuracy. The end result should be a fully functional website that allows users to assess their mental health risk, receive actionable suggestions, and track their emotional patterns over time.