Machine Learning Algorithms for Survival Analysis: Advantages, Disadvantages, and Examples

Abstract

This paper studies the application of survival machine learning models in
management for outcome prediction based on the medical literature. Twenty
survival models and over ten survival machine learning algorithms were analyzed
to find their key advantages and disadvantages. In the first half of this study, we
examine and evaluate the most prevalent models in terms of their similarities
and differences, as well as their data types and evaluation strategies. We also
highlight the concepts that all machine learning algorithms for survival analysis
must adhere to. Four machine learning algorithms from each family (trees, multitask,
kernel, and deep network) were used to analyze a breast cancer dataset and
two additional simulated datasets using the R coxed package. The results indicate
how machine learning algorithms might be used to recommend medicines and
improve population health by analyzing survival. Moreover, we establish the
ideal approaches to use based on more than twelve limitations, such as suppressed
data.