Integrating machine learning to customize chemotherapy for oral cancer patients

Abstract

Oral cancer, particularly oral squamous cell carcinoma (OSCC), poses a significant global health burden, with over 350,000 new cases annually. Despite chemotherapy being critical for advanced-stage treatment, its lack of personalization often results in inconsistent responses, severe side effects, and limited efficacy. Current methodologies, such as rule-based systems and traditional statistical models, fail to account for the complex, nonlinear interactions between patient-specific factors and drug responses, underscoring the need for advanced solutions. This paper introduces a machine learning (ML)-driven framework to optimize chemotherapy regimens for oral cancer patients. By leveraging multi-modal datasets, including genomic profiles, clinical histories, tumor burden indices, and drug toxicity metrics, the proposed model achieves remarkable results. Utilizing an ensemble of random forests and neural networks, the framework achieves an accuracy of 92 %, outperforming existing ML methods (85 %) and traditional approaches (78 %). Additionally, it demonstrates a 25 % reduction in chemotherapy-induced toxicity and a 20 % decrease in treatment costs. Key innovations include a novel efficacy-toxicity trade-off metric and adaptability through reinforcement learning for real-time regimen refinement. To address data privacy concerns, the framework incorporates federated learning, ensuring scalability across diverse healthcare systems. Preliminary results highlight a 15–20 % improvement in treatment efficacy and a 10 % reduction in adverse effects compared to existing methods. This interdisciplinary approach bridges the gap between oncology and ML, offering a robust foundation for personalized medicine. By tailoring chemotherapy regimens, this framework aims to improve survival rates, minimize treatment-related complications, and enhance the quality of life for oral cancer patients globally.