TITLE:
Cost-Optimized and Efficacy-Driven Analysis of Antidepressants in Major Depressive Disorder: A Machine Learning and Visualization Approach
AUTHORS:
Rocco de Filippis, Abdullah Al Foysal
KEYWORDS:
Antidepressants, Machine Learning, Major Depressive Disorder (MDD), Cost Efficiency, Non-Response Rates
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.2,
February
17,
2025
ABSTRACT: The treatment of major depressive disorder (MDD) often involves antidepressants, yet non-response to initial therapies remains a significant clinical and economic burden. This research aims to evaluate the comparative efficacy and cost-efficiency of 13 commonly prescribed antidepressants, spanning four major drug classes: SSRIs, SNRIs, NaSSAs, and TCAs. By employing machine learning and simulated patient data, we model non-response rates over two years, highlighting each drug’s cumulative risk trajectories. This study also investigates the direct correlation between non-response rates and estimated healthcare costs, offering insights into the economic implications of antidepressant inefficacy. The analysis reveals distinct patterns of non-response across classes, with SSRIs exhibiting the lowest cumulative risk and cost variability. Conversely, NaSSA and TCA classes demonstrate higher non-response rates, contributing to greater financial strain. Visual representations, including line plots with confidence intervals, bar plots, scatter diagrams, and box plots, provide an intuitive breakdown of risk distribution and economic impact. The primary goal of this research is to guide clinicians and policymakers in selecting cost-effective and efficacious antidepressants, ultimately improving patient outcomes while minimizing unnecessary healthcare expenditure. This study addresses the dual challenges of clinical efficacy and economic sustainability in MDD treatment by integrating statistical modelling with visual analytics. Future work will focus on incorporating real-world demographic and clinical data to enhance the precision and applicability of the findings.