Integrated Survey and Clinical Data for Risk Assessment and Survival Prediction in Colorectal Cancer Using Machine Learning

Colorectal cancer (CRC) continues to be a significant contributor to global mortality rates, with its incidence influenced by various factors including dietary habits, genetic predispositions, and age demographics. This study uses a dual-dataset strategy that combines survey and clinical data, providing a unique combination of laboratory-validated clinical information and patient-reported outcomes to improve the accuracy of CRC survival forecasts. Machine learning classifiers were utilized to assess each dataset independently and to categorize individuals into high-risk and low-risk survival groups based on age and gender. Key variables, including treatment type, survival status, and disease-free survival (DFS) events, were analyzed across the datasets, uncovering significant similarities within the respective demographic groups. The Random Forest algorithm attained the highest accuracy of 93 %…