Machine learning for rectal cancer prediction based on metabolic changes on amino acids

Machine learning is an area of Artificial Intelligence in which applying algorithms to a dataset makes it possible to predict results or even discover relationships that would be unnoticeable at first glance. Currently the amount of information available in different areas, especially in healthcare where data collection and analysis seek to define personalized medicine strategies. This is a field where using Machine Learning-based tools can assume a relevant role. This work presents a study of diverse classification algorithms in the area of machine learning applied to identification of amino acid profiles. The authors defined as a major objective to develop a new biomarker profile for prediction and prognosis of rectal cancer. The data involved in the training of classification algorithms refer to patients with metabolic diseases and rectal cancer. The best machine learning classification models will be tested to achieve the most effective decision support system for a most adequate treatment option selection in order to reduce the morbidity and mortality rate.