Similar performance of 8 machine learning models on 71 censored medical datasets: a case for simplicity

Abstract

In the analysis of medical data with censored outcomes, identifying the optimal machine learning pipeline is a challenging task, often requiring extensive preprocessing, feature selection, model testing, and tuning. To investigate the impact of the choice of pipeline on prediction performance, we evaluated 9 machine learning models on 71 medical datasets with censored targets. Only the decision tree model was consistently underperforming, while the other 8 models performed similarly across datasets, with little to no improvement from preprocessing optimization and hyperparameter tuning. Interestingly, more complex models did not outperform simpler ones, and reciprocally. ICARE, a straightforward model univariately learning only the sign of each feature instead of a weight, demonstrated similar performance to other models across most datasets while exhibiting lower overfitting, particularly in high-dimensional datasets. These findings suggest that using the ICARE model to build signatures between centers could improve reproducibility. Our findings also challenge the traditional approach of extensive model testing and tuning to improve performance.