Application of Data Science in Colorectal Cancer Diagnosis

Abstract

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers worldwide, with early detection being crucial for improving survival rates. Recent advancements in data science, including machine learning (ML), deep learning (DL), and big data analytics, have significantly enhanced CRC diagnosis by improving accuracy, efficiency, and predictive capabilities. This paper examines the application of data science techniques in CRC diagnosis, with a focus on image analysis, genomic data interpretation, and predictive modeling. We review various ML and DL algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and random forests, applied to histopathological images, colonoscopy videos, and biomarker datasets. Additionally, we discuss challenges such as data heterogeneity, model interpretability, and ethical considerations. Our findings suggest that data science holds immense potential in revolutionizing CRC diagnosis, leading to earlier detection and personalized treatment strategies.