Predictive Modeling of Colorectal Tumor Growth and Progression with Machine Learning

This research investigates the feasibility and potential applications of machine learning methods in forecasting the initiation and advancement of colorectal cancer. The proposed work investigates the efficacy of a machine learning model in customizing remedies according to each patient's diagnosis and stage of colorectal cancer. The machine learning model performed admirably when loaded with a "ColoGenIma" dataset comprising genomic, imaging, and clinical data. It is essential to highlight the model's remarkable accuracy of 97% in forecasting treatment outcomes. The results indicate that the proposed machine-learning model can provide individualized treatment recommendations for patients with colorectal tumors. The study's results shed light on the potential for data-driven models to revolutionize the healthcare industry by improving precision medicine, which enhances patient care and outcomes.