Comparison of Different Machine Learning Models for Predicting Long-Term Overall Survival in Non-metastatic Colorectal Cancers

Abstract

INTRODUCTION: In recent years, machine learning (ML) methods have gained significant popularity among medical researchers interested in cancer. We aimed to test different (ML) models to predict both overall survival and survival at specific time points in patients with non-metastatic colorectal cancer (CRC). METHODS: The clinicopathological and treatment data of non-metastatic CRC patients with more than 10 years of follow-up at a single center were retrospectively reviewed. 1, 2, 3, 5, and 10-year survival rates for all patients and stages I-III were statistically calculated using the Kaplan-Meier method. Five distinct machine-learning algorithms were employed to develop predictive models for patient survival at five designated time points. RESULTS: A total of 498 patients were included in the study. The decision tree model had the highest area under the curve (AUC) for 1-year survival prediction (0.89). The ensemble model had the highest AUC for predicting 2-year, 3-year, and 5-year survival predictions (0.86, 0.92, and 0.89, respectively), while the support vector machine model had the highest AUC (0.84) for predicting 10-year survival. When considering the stages separately and assessing survival for the designated time intervals, the accuracy of all five models was found to be similar, ranging around 70% or higher. CONCLUSION: ML models can predict short- and long-term survival in patients with CRC, both for the overall patient population and when stratified by stage.

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