Personalized Medicine: Predictive Analytics for Drug Response Using Machine Learning Models

Abstract

Personalized medicine represents a transformative approach in healthcare, enabling tailored therapeutic interventions based on individual patient characteristics. Predictive analytics powered by machine learning (ML) has emerged as a cornerstone of this paradigm, leveraging vast datasets to predict drug responses with unparalleled accuracy. This paper explores the integration of ML models in personalized medicine, focusing on their application in predicting patient-specific drug efficacy and adverse reactions. By utilizing diverse data sources, including genomic, proteomic, and clinical data, ML algorithms offer novel insights into complex biological interactions. Recent advances, such as deep learning and ensemble techniques, have shown exceptional promise in capturing nonlinear relationships within high-dimensional datasets [1].This study presents a comprehensive review of state-of-the-art ML methodologies employed in drug response prediction, highlighting their strengths and limitations. Additionally, it proposes a hybrid ML framework combining feature selection, supervised learning, and explainable AI techniques to enhance model interpretability and clinical utility. The proposed framework is validated using publicly available pharmacogenomic datasets, achieving improved prediction accuracy and reduced computational overhead compared to traditional approaches [2].Challenges, such as data heterogeneity, model bias, and regulatory considerations, are critically discussed, emphasizing the need for standardized data preprocessing pipelines and robust evaluation metrics. The integration of ML-driven predictive analytics in clinical workflows is poised to accelerate the realization of precision medicine, improving patient outcomes while minimizing trial-and-error approaches in drug prescribing.

DOI - https://doi.org/10.65525/SVUP.9788199565418.2026.154-158