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International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
             4.  Deployment and Impact Assessment:
               Launch the platform on a larger scale.
               Evaluate outcomes through clinical studies and user satisfaction metrics.
             5.  Expected Outcomes:
               Early and accurate detection of mental health conditions.
               Greater accessibility to mental health care resources.
               Empowered decision-making for clinicians with predictive analytics.
               Increased engagement and adherence to therapeutic interventions.
             V.     PERFORMANCE EVALUATION
             1.  Data Collection:
             To evaluate Mental Well’s performance, data was collected from a diverse cohort of 5,000 users over 12 months. Users were
             categorized into three groups:
             1.  Preventive Users: Individuals without diagnosed mental health issues but at risk.
             2.  Management Users: Individuals managing diagnosed conditions.
             3.  Clinical Users: Individuals using MentalWell as part of their professional therapy.
             2.  Evaluation Metrics:
             Performance was assessed using the following criteria:
             1.  Accuracy: Precision in detecting mental health conditions based on standardized diagnostic tools.
             2.  Engagement: User interaction rates and adherence to recommendations.
             3.  Outcome Improvement: Measurable changes in mental health status using validated scales like GAD-7 and PHQ-9.
             4.  User Satisfaction: Feedback scores on usability and trust.
             3.  Analytical Tools:
             Machine learning models were evaluated for their diagnostic accuracy. Statistical analysis, including ANOVA and regression
             models, was used to measure outcome improvements and engagement levels.
             4.  Results:
             Accuracy and Detection-
             Mental Well achieved an average diagnostic accuracy of 89% compared to clinical diagnoses, with higher accuracy for anxiety
             (92%) and moderate accuracy for complex disorders like bipolar disorder (78%).
             Engagement and Adherence
               Engagement Rates: 72% of users interacted with the platform at least three times weekly.
               Adherence to Recommendations: 65% of users consistently followed AI-generated interventions over six months.
               Preventive Users: 68% reported reduced stress and anxiety levels.
               Management Users: 54% showed significant improvement in clinical scores (e.g., PHQ-9 > 5-point reduction).
               Clinical Users: 78% found Mental Well’s integration with their therapy beneficial.
             5.  User Satisfaction:
             User feedback highlighted high satisfaction rates, with an average score of 4.6/5. Key strengths noted were ease of use,
             personalized recommendations, and timely responses.
             6.  Discussion:
             The findings suggest that MentalWell effectively supports mental health prevention and management. Its high diagnostic
             accuracy and engagement rates underscore its potential as a scalable solution for mental health care. However, certain
             limitations were noted:
               Complex Conditions: Moderate accuracy in detecting multifaceted disorders like bipolar disorder.
               Digital Divide: Limited accessibility for populations without internet or technology proficiency.
             7.  Future improvements could focus on:
             1.  Enhancing detection algorithms for complex conditions.
             2.  Expanding language and cultural inclusivity.
             3.  Strengthening data privacy and ethical safeguards.


















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