1. Introduction
MSI, cancers, colorectal cancer, CRC, heterogeneous diseases, NB, deaths, Colorectal cancer
COLORECTAL CANCER, CANCERS, MICROSATELLITE INSTABILITY, MAY, REGRESSION, COLORECTAL CANCER
Academic Editor: Xing NiuChemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test, Colorectal cancer (CRC) is one of the most common cancers worldwide and the second leading cause of cancer-related deaths [Microsatellite instability (MSI) is the abnormal shortening or lengthening of 1–6 repeat base pair units of DNA caused by inactivation of the MMR system [Recently, artificial intelligence has become a research hotspot in medicine due to the potential to achieve high-precision automated diagnosis of heterogeneous diseases. Skrede et al. [Based on simple clinicopathological indicators and with reference to previous studies, four machine learning models and a logistic regression model were developed in this study to predict CRC lacking DNA MMR, aid clinicians in identifying MMR status, and provide a reference for a precise treatment plan for patients.