combining machine learning and early detection of colon cancer: a novel approach

Early detection is crucial to improving treatment outcomes in colon cancer, one of the most common types of cancer worldwide. it is often difficult to diagnose colon cancer via traditional methods such as colonoscopies and biomarker tests, and these tests may only be accurate up to a certain point. the use of new technologies such as machine learning in the analysis of clinical, image, and genetic data has been able to create a revolution in the faster and more accurate diagnosis of this disease. this review study investigates the application and effect of combining machine learning techniques in early and accurate diagnosis of colon cancer. the main goal is to analyze how to use machine learning algorithms to improve the accuracy and speed of diagnosing this cancer, especially in the early stages. by analyzing large and complex data, machine learning algorithms extract hidden patterns from medical imaging results such as ct and mri, molecular data, and even genetic data. further, these algorithms can be combined with biomarker data and clinical characteristics to develop advanced predictive models capable of detecting early-stage colon cancer, which will reduce the incidence of false diagnoses and increase the speed of treatment by developing highly accurate predictive models. several previous studies have shown that machine intelligence can be used along with existing diagnostic methods to enhance screening, help identify patients at risk, and help doctors make better clinical decisions based on the results.