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MindSight - Mental health and well-being surveillance, assessment and tracking solution

  • Unique Paper ID: 178500
  • Volume: 11
  • Issue: 12
  • PageNo: 3434-3440
  • Abstract:
  • MindSight is a web-based mental health assessment platform designed to help users evaluate their psychological well- being by self-assessing levels of stress, anxiety, and depression. Developed as part of the final year project ISR-G03 at Presidency University, the application leverages the scientifically validated DASS-21 (Depression Anxiety Stress Scales) questionnaire to collect user input across 21 questions—seven each for stress, anxiety, and depression. The system uses multiple machine learning models trained on the publicly available DASS-21 dataset to predict the severity levels of each condition, categorized as Normal, Mild, Moderate, Severe, or Extremely Severe. Among the models tested—including Logistic Regression, Decision Tree, Random Forest, XGBoost, and others—the Support Vector Machine (SVM) model achieved the highest accuracy and was selected for final deployment. The platform features a user-friendly interface built using HTML and CSS, with a Python Flask backend and scikit- learn for ML integration. Predictions are visually communicated through intuitive, color-coded indicators, ensuring accessible and engaging user experience. MindSight aims to support early mental health intervention, especially among children, by providing a quick, anonymous, and reliable self-assessment tool.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 3434-3440

MindSight - Mental health and well-being surveillance, assessment and tracking solution

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