A genetic algorithm-based Auto-ML system for survival analysis

Survival analysis methods aim to develop a model predicting the time passed until the occurrence of an event (e.g. death) for each subject. This requires coping with censored values of the target variable (time until the event), i.e., for some subjects, the value of the target variable is only partly known - for example, if the subject left the study before the event of interest was observed. Automated Machine Learning (Auto-ML) aims at automatically selecting the best algorithm and its best hyperparameter settings for a given input dataset. This work proposes the first Auto-ML system designed specifically for survival analysis. The system is based on a Genetic Algorithm, and experiments with 9 biomedical datasets have shown that overall the system obtained higher predictive accuracies than three well-established baseline survival analysis methods.