Machine Learning Approaches in Predicting Cancer Drug Response

Abstract

Cancer treatment outcomes vary widely among patients due to tumour heterogeneity, genetic diversity, and environmental factors. Predicting drug response accurately is a central challenge in precision oncology. Machine learning (ML) has emerged as a powerful tool to integrate multi-omics data and clinical information to forecast therapeutic responses. This paper provides a comprehensive and in-depth analysis of machine learning approaches used in predicting cancer drug response. It discusses data sources, preprocessing strategies, feature engineering, algorithmic models, validation techniques, and real-world applications. The study also highlights challenges such as data imbalance, interpretability, and reproducibility, and explores emerging directions including explainable AI, federated learning, and digital twin models. The integration of ML into oncology is expected to revolutionize personalized medicine, improve treatment efficacy, and reduce adverse effects.