Integrating Machine Learning Regression Models for Enhanced Cancer Prediction

Colorectal cancer is one of the most prevalent and deadly forms of cancer worldwide, necessitating effective treatment strategies to improve patient outcomes. Objective response, a critical measure in clinical oncology, refers to the observable and quantifiable change in tumor size following treatment. This study aims to predict changes in lesion size, specifically focusing on the percentage increase and decrease in the size of three types of lesions: new lesions, target lesions, and non-target lesions. By analyzing a dataset containing relevant clinical data, we seek to develop predictive models that can accurately forecast the progression or regression of lesion sizes. Such predictions are crucial for evaluating treatment efficacy and tailoring personalized treatment plans for colorectal cancer patients.