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DEVELOPING A CHAT SUPPORT SYSTEM CONNECTING USERS WITH MENTAL HEALTH PROFESSIONALS USING RANDOM FOREST
Abstract:
The rapid evolution of technology has opened new avenues for providing mental health support, and this study proposes the development of a chat support system connecting users with mental health professionals. Leveraging the capabilities of machine learning, particularly the Random Forest algorithm, the system aims to enhance accessibility and facilitate timely interventions for individuals seeking mental health assistance. The Random Forest algorithm, known for its versatility and accuracy, will be employed to analyze user input, understand the context, and connect users with appropriate mental health professionals based on their needs. The system’s development will involve comprehensive data preprocessing, feature engineering, and model training. Through the integration of natural language processing and a user-friendly interface, the proposed chat support system aspires to bridge the gap between individuals in need of mental health support and qualified professionals, fostering a more accessible and responsive mental health ecosystem. The study anticipates contributing to the ongoing efforts to leverage technology for improving mental health services and promoting overall well-being.
TABLE OF CONTENT
Chapter 1: Introduction
1.1 Background
1.1.1 Evolution of Mental Health Support Systems
1.1.2 Rationale for Developing a Chat Support System
1.2 Statement of the Problem
1.2.1 Accessibility Challenges in Mental Health Support
1.2.2 Importance of Timely Interventions
1.3 Research Aim and Objectives
1.3.1 Aim of Developing a Chat Support System
1.3.2 Specific Objectives
1.4 Significance of the Study
1.5 Scope and Limitations
1.5.1 Scope of Chat Support System Features
1.5.2 Limitations of the Study
1.6 Organization of the Thesis
Chapter 2: Literature Review
2.1 Evolution of Mental Health Support Systems
2.2 Technological Interventions in Mental Health
2.2.1 Telemedicine and Remote Support
2.2.2 Chatbots and Natural Language Processing
2.3 Challenges in Current Mental Health Support Systems
2.4 The Role of Machine Learning in Mental Health
2.4.1 Random Forest Algorithm
2.4.2 Applications of Random Forest in Healthcare
2.5 Gaps in Existing Literature
2.6 Conceptual Framework
Chapter 3: Methodology
3.1 System Architecture
3.1.1 User Interface Design
3.1.2 Backend Infrastructure
3.2 Data Collection and Preprocessing
3.2.1 User Input Handling
3.2.2 Data Privacy and Security Measures
3.3 Feature Engineering
3.3.1 Extracting Relevant Features for User Profiling
3.3.2 Contextual Analysis
3.4 Random Forest Model Training
3.4.1 Dataset Selection and Preparation
3.4.2 Model Hyperparameter Tuning
3.5 Integration of Natural Language Processing
3.5.1 Understanding and Processing User Input
3.5.2 Response Generation
3.6 Ethical Considerations
3.6.1 Ensuring User Consent and Confidentiality
3.6.2 Addressing Bias in Algorithmic Decision-Making
Chapter 4: Implementation and Results
4.1 System Implementation
4.1.1 Software and Hardware Requirements
4.1.2 User Testing and Feedback
4.2 Evaluation Metrics
4.2.1 Accuracy and Precision
4.2.2 User Satisfaction
4.3 Results and Findings
4.3.1 Effectiveness of Random Forest in User Profiling
4.3.2 User Engagement and Satisfaction Levels
4.3.3 Challenges Encountered during Implementation
Chapter 5: Discussion and Conclusion
5.1 Interpretation of Results
5.1.1 Implications of Random Forest in Mental Health Profiling
5.1.2 User Feedback and System Improvements
5.2 Contributions and Future Directions
5.2.1 Advancements in Mental Health Support Systems
5.2.2 Integration of Emerging Technologies
5.3 Conclusion
5.3.1 Recapitulation of Findings
5.3.2 Final Remarks and Recommendations
References
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