Page 493 - Emerging Trends and Innovations in Web-Based Applications and Technologies

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.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 483