AetherMind is a machine learning–based mental health support platform designed to detect early signs of mental health issues such as anxiety, depression, and mood disorders, and to provide personalized interventions and continuous support. The system aims to address delayed and biased diagnoses by leveraging data-driven insights from user behavior, questionnaires, and socio-demographic information.
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System Architecture: The system follows a full-stack, modular architecture:
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Frontend: Built using HTML, CSS, JavaScript, and Bootstrap to provide a responsive and user-friendly interface for data input, task suggestions, motivational content, and chatbot interaction.
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Backend: Implemented using Flask, PHP, and Node.js to handle API requests, session management, business logic, and communication between frontend and models.
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Database: MySQL is used for secure storage of user profiles, mental health scores, tasks, recommendations, and system data.
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Machine Learning Layer:
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Data preprocessing includes cleaning, handling missing values, normalization, and feature encoding.
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Models such as Linear Regression, Naive Bayes, KNN, and Random Forest are trained to generate a Mental Health Score.
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Hyperparameter tuning is performed using GridSearchCV.
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Chatbot: NLTK-based sentiment analysis is used to provide empathetic responses and emotional support.
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External Services: Google Maps API is integrated to locate nearby psychologists when professional help is needed.
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Key Features
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Mental health score generation based on user input and behavioral data
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Personalized daily task recommendations and motivational quotes
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Chatbot for emotional support using sentiment analysis
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Interactive drawing feature with motivational feedback
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Weekly/monthly mental health insights (planned enhancement)
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Location-based psychologist recommendations
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Admin dashboard for content and system management
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[Note: AetherMind demonstrates how machine learning, NLP, and web technologies can be combined to create an early mental health assessment and support system. Although not a clinical diagnostic tool, it serves as a preventive, assistive platform that empowers users to monitor their mental well-being, encourages early intervention, and provides accessible emotional support. Future enhancements aim to improve prediction accuracy, introduce gamification, and add long-term mental health progress tracking.]