Machine Learning Applications in the Diagnosis and Treatment of Rectal Cancer

Abstract

Diagnosing rectal cancer early, and accurately and tailoring treatment protocols are global challenges as this form of cancer is common and detrimental to many people which is found worldwide. Fortunately, the high pace of developments in machine learning (ML) technologies has triggered exploration into this field, expanding the possibilities for improving diagnostic accuracy, assessing prognosis, and selecting treatment rationales. In this paper, we review the current state of implementation of ML methods for colorectal cancer, consider related works, analyze existing systems, and present our ML-based system aimed at enhancing diagnostic and predictive capabilities. Molecular genetic imaging, immunohistochemistry, and clinical data are integrated into this model to enable a holistic, data-driven approach to the management of rectal cancer. The experimental results indicate how effective the model is in terms of potential utility for clinical practice as its accuracy in predicting tumorous stage and treatment response is high