Predicting Bipolar Disorder Treatment Outcomes with Machine Learning: A Comprehensive Evaluation of Random Forest, Gradient Boosting, and Ensemble Approaches

Accurate prediction of treatment response in bipolar disorder patients with comorbid obsessive-compulsive disorder (OCD) is essential to improving clinical outcomes and minimizing ineffective interventions. The complex interplay between bipolar disorder and OCD often complicates pharmacological treatment, leading to inconsistent results. This study aims to leverage machine learning (ML) techniques to develop predictive models that enhance the precision of quetiapine monotherapy outcomes. The primary objective of this research is to evaluate the performance of Random Forest (RF) and Gradient Boosting (GB) classifiers in forecasting treatment response, thereby addressing the unpredictability in clinical decision-making. In addition, an ensemble model combining the strengths of RF and GB is developed to optimize predictive accuracy. By incorporating demographic, psychometric, and pharmacological data, the models are trained and validated on a dataset of 300 patients diagnosed with bipolar disorder and comorbid OCD. Comprehensive model evaluations are conducted through visual analyses, including confusion matrices, feature importance plots, precision-recall curves, calibration plots, learning curves, and ROC curves. These visualizations not only reveal the models’ predictive performance but also highlight the key features influencing treatment response. Results indicate that the ensemble model consistently outperforms individual classifiers, achieving higher AUC scores and lower false negative rates, which are critical for minimizing missed treatment opportunities. The findings of this study provide a robust framework for integrating ML into psychiatric care, supporting more personalized and accurate treatment plans. This research underscores the transformative potential of predictive analytics in enhancing therapeutic strategies for complex psychiatric conditions.