Enhanced Mental Health Risk Prediction using ML Algorithms
DOI:
https://doi.org/10.15662/IJEETR.2026.0802035Keywords:
Mental Health Prediction, Machine Learning, Risk Assessment, Data Preprocessing, Feature Selection, Classification Algorithms, Logistic Regression, Decision Tree, Support Vector Machine (SVM), Model Evaluation, Accuracy, Precision and Recall, F1-Score, Early Detection, Predictive AnalyticsAbstract
Mental health disorders are becoming increasingly prevalent worldwide, making early detection and intervention essential for effective treatment and prevention. This project focuses on developing an enhanced mental health risk prediction system using machine learning algorithms. In Phase 2, the primary objective is to design and implement data preprocessing techniques and apply suitable machine learning models to identify individuals at risk of mental health issues
The system utilizes a dataset containing various psychological, behavioral, and demographic features such as stress levels, sleep patterns, work environment, and lifestyle habits. During this phase, data cleaning, handling missing values, normalization, and feature selection techniques are performed to improve data quality and model performance. Multiple machine learning algorithms such as Logistic Regression, Decision Tree, and Support Vector Machine (SVM) are implemented and compared to identify the most accurate model
Performance evaluation metrics including accuracy, precision, recall, and F1-score are used to assess the effectiveness of each model. The results obtained in this phase help in selecting the optimal algorithm for further enhancement in subsequent phases. This phase lays the foundation for building a reliable and scalable mental health prediction system that can assist in early diagnosis and preventive care
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