Improving Mental Health Diagnostics through Advanced Algorithmic Models: A Case Study of Bipolar and Depressive Disorders

Abstract

This study explores the efficacy of a voting classifier integrating K-Nearest Neighbors (K-NN), Gaussian Naive Bayes (GNB), and Random Forest algorithms in diagnosing bipolar and depressive disorders. Utilizing a dataset of 120 psychology patients exhibiting 17 essential symptoms, the research employs a 5-fold cross-validation method to assess the model's diagnostic performance. Results indicate variability in accuracy (66.67% to 91.67%), precision (66.46% to 93.75%), recall (identical to accuracy), and F1-Scores (65.96% to 91.43%) across folds, demonstrating the model's robustness and potential to enhance psychiatric diagnostic processes. The findings suggest that the voting classifier significantly outperforms traditional diagnostic methods, offering a promising tool for more accurate and efficient mental health diagnostics. This research contributes to the burgeoning field of machine learning applications in mental health care, highlighting the potential of ensemble methods in addressing the complexities of psychiatric diagnosis. Given the limitations related to data diversity and model sensitivity, future research should focus on employing larger, more varied datasets and exploring the integration of additional algorithms to further refine diagnostic accuracy. This study lays the groundwork for advancing mental health diagnostics through innovative machine learning techniques.

Keywords

• Machine Learning
• Bipolar Disorder
• Depressive Disorder
• K-Nearest Neighbors
• Gaussian Naive Bayes
• Random Forest