Using Machine Learning for Prevention and Early Detection of Colorectal Cancer

Colorectal cancer is one of the most important causes of death worldwide, and early detection and prevention can significantly reduce the risk of death from the disease. Machine learning (ML) has become a powerful tool in the last few years for analyzing massive amounts of medical data and developing techniques for predicting and preventing colorectal cancer as a result of the rapid growth of medical data and advancements in artificial intelligence. The objective of this review is to investigate how machine learning techniques can be used to detect, predict, and customize colorectal cancer prevention early. Methods: The data to be analyzed for this study was collected through a systematic search across major academic databases, including PubMed and Scopus, from ۲۰۱۵ to ۲۰۲۴. To identify relevant literature, keywords such as “machine learning,” “colorectal cancer,” “early detection,” “prevention,” and “Artificial Intelligence in Healthcare” were used with the search engines. Results: The use of machine learning algorithms for analyzing medical data, like genetic data or images from a colonoscopy, can include random forests, artificial neural networks, support vector machines, and deep learning. In addition to their use in identifying hidden patterns associated with increased risk of colorectal cancer, these algorithms were also examined for their ability to identify complex patterns associated with increased risk. Previous studies have highlighted the advantages of this technology by emphasizing how it can be used to optimize screening methods, identify non-invasive biomarkers, and predict patients' responses to preventative approaches such as lifestyle changes or pharmaceutical interventions. A common application of machine learning is analyzing colonoscopy images using deep learning algorithms, such as convolutional neural networks (CNNs), since these algorithms can learn from images without requiring explicit rules. With the help of these algorithms, a colonoscopy image can be automatically identified for polyps and small tumors with high accuracy, and the polyps and lesions classified as benign or malignant lesions are detected automatically. There has been some evidence that CNN models can detect precancerous lesions even with a ۹۴?curacy, which is far more accurate than manual methods, such as those used in one of the studies. Moreover, machine learning can also be applied to analyze genetic data and molecular profiles. Using supervised learning algorithms, it was possible to identify patterns associated with mutations in genetic genes such as APC and KRAS, which are associated with colorectal cancer. Moreover, the combination of biological data with clinical data can be used to predict the likelihood of a patient responding to targeted treatments or immunotherapy with the help of machine learning. Practical challenges involved in applying these methods are also discussed. Among them are the need for big data, the quality of the input data, issues relating to the interpretability and reliability of the machine learning models, and other issues. Conclusion: Therefore, machine learning can significantly improve the prevention of colorectal cancer by providing more accurate prediction methods, optimizing screening programs, and even allowing at-risk patients to choose from a wider range of treatment options in order to develop more personalized treatment programs.

کلیدواژه ها:

Machine learning ، Prevention ، Colorectal cancer ، Diagnosis ، Artificial intelligence