Personalized Prediction of Survival Rate with Combination of Penalized Cox Models and Machine Learning in Patients with Colorectal Cancer

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Abstract Background The investigation into individual survival rates within the patient population was typically conducted using the Cox proportional hazards model, with geometric black box models not being employed Aims We aims to evaluate the performance of machine learning algorithm in predicting survival rates more than 5 years for individual patients with colorectal cancer. Methods A total of 475 patients with CRC and complete data who had underwent surgery for colorectal cancer were analyze to measure individual's survival rate more than 5 years using a machine learning based on penalized Cox regression. We conducted thorough calculations to measure the individual's survival rate more than 5 years for performance evaluation. The receiver operating characteristic (ROC) curves for the LASSO penalized model, the SCAD penalized model, the unpenalized model, and the RSF model were analyzed. Results The least absolute shrinkage and selection operator penalized model displayed a mean AUC of 0.67 ± 0.06, the smoothly clipped absolute deviation penalized model exhibited a mean AUC of 0.65 ± 0.07, the unpenalized model showed a mean AUC of 0.64 ± 0.09. Notably, the random survival forests model outperformed the others, demonstrating the most favorable performance evaluation with a mean AUC of 0.71 ± 0.05. Conclusions Penalized Cox model is more efficient and leads to a more generalized model selection compared to the unpenalized Cox model as a prognosis prediction model for CRC. The results indicated that the random forest model, a black box model, outperformed the penalized Cox model in terms of performance.