A retrospective analysis of key predictors and patient outcomes: Using artificial intelligence for precision survival prediction in colorectal cancer.
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OBJECTIVE: The study aims to identify critical predictors of mortality and evaluate the performance of different artificial intelligence (AI) models among patients with colorectal cancer (CRC). Furthermore, the study also sought to enhance our comprehension of survival outcomes by identifying key predictors and evaluating the accuracy of AI-driven prediction methods for patients with CRC. METHODS: The study employed a retrospective-predictive design, using data from the electronic health records of patients with colorectal cancer (CRC) admitted between 2016 and 2023. Among the eight AI models created by the SPSS Modeler version 18.0, the Bayesian network model was the most effective of the eight models in this study. RESULTS: The researchers identified the most relevant variables associated with mortality among patients with CRC through data visualization. The study analysed 1,159 colorectal cancer patients, with 45.7% living up to six years and 54.3% living between seven and 16 years post-diagnosis. The Bayesian network AI model identified stage, age, recurrence, sex, marital status, and smoking status as key predictors. CONCLUSION: This study model's structure emphasizes these predictors' interconnectedness because parent nodes directly connect to child nodes. The model shows how age, smoking status, marital status, cancer stage, and recurrence affect patient survival. The model clarifies these variables' interactions.