Depressive disorders are complex, multifactorial conditions that exhibit significant variability in treatment response, often influenced by gender differences. This study leverages advanced machine learning (ML) techniques to predict antidepressant response to sertraline and imipramine, addressing the pressing need for personalized treatment strategies. By employing the Synthetic Minority Oversampling Technique (SMOTE), the research overcomes class imbalance—a common limitation in clinical datasets—ensuring fair representation of minority classes and enhancing predictive reliability. Rigorous hyperparameter tuning further refines model performance, maximizing accuracy and stability. Key clinical and demographic factors, such as BMI, baseline Hamilton Depression Rating Scale (HAM-D) scores, age, and gender, are analyzed to identify their relative importance in predicting treatment outcomes. Visual analytics, including confusion matrices, feature importance rankings, receiver operating characteristic (ROC) curves, and pair plots, provide transparent and interpretable insights into the model’s performance, shedding light on the nuanced relationships between patient characteristics and drug efficacy. This study introduces a comprehensive, data-driven framework designed to optimize antidepressant prescriptions by individualizing treatment pathways for diverse patient populations. The integration of ML-driven predictive analytics into psychiatric care represents a significant advancement in the pursuit of precision medicine, offering clinicians valuable tools to enhance patient outcomes, minimize adverse effects, and ultimately improve long-term mental health care. By addressing the gap between generalized antidepressant prescriptions and patient-specific needs, this research lays the foundation for a paradigm shift toward tailored, evidence-based interventions that account for gender-specific response patterns, furthering the goals of personalized psychiatry.
Cite this paper
Filippis, R. D. and Foysal, A. A. (2025). Advanced Machine Learning Models for Gender-Specific Antidepressant Response Prediction: Overcoming Data Imbalance for Precision Psychiatry. Open Access Library Journal, 12, e2895. doi: http://dx.doi.org/10.4236/oalib.1112895.
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