Machine Learning-based Risk Stratification for Precision Oncology: A Framework for Personalized Cancer Care

Abstract

This study proposes a machine learning-based risk stratification framework for precision oncology to guide personalized cancer care decisions. By integrating multi-omics data, including genomic profiles, transcriptomic signatures, and clinical variables, our model predicts patient-specific cancer prognosis, treatment response, and risk of disease recurrence. Implementation of this framework in clinical practice enables oncologists to tailor treatment strategies based on individual patient characteristics, thereby improving treatment outcomes and optimizing resource utilization in cancer care delivery.