Enhancing Colorectal Cancer Diagnosis With Machine Learning Algorithms

Abstract

Colorectal cancer (CRC) is a significant public health concern, demanding accurate and economic diagnostic technologies to provide rapid treatment and better patient outcomes. Existing diagnostic systems frequently have flaws in terms of accuracy, efficiency, and subjectivity due to the dependence heavily on manual interpretation of patient data. To solve these issues, the research proposes a revolutionary strategy that uses machine learning (ML) algorithms to revolutionize CRC detection. The proposed system distinguishes itself by using strong machine-learning algorithms to objectively and accurately assess vast amounts of patient data. Comprehensive data collection, preprocessing, feature selection and extraction, algorithm selection and training, ensemble learning, and a thorough evaluation utilizing various performance indicators are all important aspects. The data and analysis show significant improvements over existing systems, including greater sensitivity (0.85), specificity (0.88), accuracy (0.87), precision (0.82), recall (0.85), F1-score (0.83), and AUC-ROC (0.92). When compared to standard techniques, the proposed system reduces diagnosis time, improves workflow, reduces human error, increases scalability, and optimizes resource utilization. The research emphasizes the potential of ML algorithms to improve CRC diagnosis, pave pathways for customized therapy, and better patient care in clinical practice.