Enhancing Cure Rate Analysis Through Integration of Machine Learning Models: A Comparative Study

Cure rate models have been thoroughly investigated across various domains, encompassing medicine, reliability, and finance. The merging of machine learning (ML) with cure models is emerging as a promising strategy to improve predictive accuracy and gain profound insights into the underlying mechanisms influencing the probability of cure. The current body of literature has explored the benefits of incorporating a single ML algorithm with cure models. However, there is a notable absence of a comprehensive study that compares the performances of various ML algorithms in this context. This paper seeks to address and bridge this gap. Specifically, we focus on the well-known mixture cure model and examine the incorporation of five distinct ML algorithms: extreme gradient boosting, neural networks, support vector machines, random forests, and decision trees. To bolster the robustness of our comparison, we also include cure models with logistic and spline-based regression. For parameter estimation, we formulate an expectation maximization algorithm. A comprehensive simulation study is conducted across diverse scenarios to compare various models based on the accuracy and precision of estimates for different quantities of interest, along with the predictive accuracy of cure. The results derived from both the simulation study, as well as the analysis of real cutaneous melanoma data, indicate that the incorporation of ML models into cure model provides a beneficial contribution to the ongoing endeavors aimed at improving the accuracy of cure rate estimation.