Chapter 16 - Machine learning-based cure model in engineering reliability

Abstract

In this chapter, a two-component mixture cure model (MCM) is considered. The first component of the MCM that describes the cure rate, or the incidence, is modeled using a machine learning (ML)-based approach. The second component of the MCM that describes the survival distribution of the uncured, or the latency, is modeled using the Cox's proportional hazards structure. The approach presented here is unlike the standard approaches where the probability of cure is modeled using a logistic link function. For the estimation of model parameters, an expectation maximization algorithm is developed in conjunction with the Platt scaling method, which is used to convert the ML outputs to posterior probabilities of cure. When the classification boundary, with respect to classifying the cured and uncured units, is nonlinear we show that the ML-based modeling results in more accurate and precise estimates of the cured probability when compared to the logistic regression-based modeling. This further results in improved predictive accuracy of cure. Interestingly, we find that improving the estimation results related to the incidence also improves the latency estimation results. Finally, we illustrate an application of the ML-based MCM using a data on the lifetime of Kevlar-49 wrapped pressure vessels subject to different stress levels.