Machine learning for predicting survival of colorectal cancer patients

Colorectal cancer is one of the most incident types of cancer in the world, with almost 2 million new cases annually. In Brazil, the scenery is the same, around 41 thousand new cases were estimated in the last 3 years. This increase in cases further intensifies the interest and importance of studies related to the topic, especially using new approaches. The use of machine learning algorithms for cancer studies has grown in recent years, and they can provide important information to medicine, in addition to making predictions based on the data. In this study, five different classifications were performed, considering patients' survival. Data were extracted from Hospital Based Cancer Registries of Sao Paulo, which is coordinated by Fundacao Oncocentro de Sao Paulo, containing patients with colorectal cancer from Sao Paulo state, Brazil, treated between 2000 and 2021. The machine learning models used provided us the predictions and the most important features for each one of the algorithms of the studies. Using part of the dataset to validate our models, the results of the predictors were around 77% of accuracy, with AUC close to 0.86, and the most important column was the clinical staging in all of them.

Automated summary

Colorectal cancer is a prevalent cancer worldwide, with about 2 million new cases annually. Machine learning algorithms have been increasingly used in cancer studies, providing important information and predictions based on data. This study used patient data from São Paulo, Brazil and five different classification algorithms to predict survival in colorectal cancer patients, achieving an accuracy of approximately 77% with an AUC of 0.86, with clinical staging being the most important feature.