Optimizing Mental Health Prediction by Fine-Tuning Decision Classifier Parameters for Enhanced Accuracy

Abstract

This study is based on the field of mental health assessment, where various machine learning algorithms were analysed to predict mental health conditions using a large dataset containing different features including demographic, lifestyle, and behavioural information etc. Following a robust level of experimentation, Decision Trees, SVM, Random Forest, and Logistic Regression models were trained and tested for performance, where the Decision Trees model achieved the highest accuracy of 83.05%. However, beyond performance metrics, research has been conducted on feature engineering to improve interpretability and pinpoint the most impactful features on mental health outcomes. Excessive hyperparameter tuning and cross-validation techniques were implemented to maximize predictive abilities. The results show the potential of machine learning algorithms in identifying the potential patients with mental health disorders with a high degree of accuracy. The feature importance analysis suggests detailed guidelines for health care practitioners and policy-makers to design possible solutions and services for intervention and treatment of such individuals. Thus, the study assists to combine a data-driven approach with other traditional check-ups to used paradigms for mental health diagnosis. Combined with traditional methods, AI-driven technology-based models can improve early discoverability and assistance and eventually better patient conditions. The study finds its value in literature as it explores applications of AI in mental health assessment, remembering the processes and methods previously utilized to develop revolutions in mental health diagnosis and treatment.