Machine Learning-Driven Personalized Cancer Treatment: Evaluating Supervised and Reinforcement Learning Approaches

This research explores the application of machine learning techniques in recommending personalized cancer treatments, comparing supervised learning and reinforcement learning approaches. By leveraging historical patient data, the study predicts optimal treatment strategies, with models like XGBoost and Artificial Neural Networks demonstrating superior accuracy. Feature selection methods identified key patient-specific factors influencing treatment outcomes, ensuring model interpretability and relevance. Rigorous statistical validation confirmed the substantial advantage of advanced machine learning models in improving predictive reliability. Among these, XGBoost emerged as particularly effective, highlighting its potential in optimizing personalized therapy decisions. The findings emphasize the transformative role of machine learning in precision oncology, paving the way for data-driven treatment recommendations. Future research should integrate multi-modal data sources, including genomic, imaging, and clinical records, to enhance predictive accuracy. Additionally, incorporating explainable AI techniques will improve transparency and foster clinical trust, ultimately facilitating real-world implementation. This study underscores the promise of machine learning in revolutionizing cancer treatment personalization, guiding more informed and effective therapeutic strategies.