A Comparison of Feature Selection Using SHAP-Values for Tree and Non-tree-Based Models

In healthcare, clinical trial datasets are often high-dimensional and have small sample sizes, increasing the risk of overfitting. Furthermore, research in this context usually focuses not only on the accuracy of the resulting models but also on their explainability. Feature selection can mitigate overfitting and provide valuable insights into relevant features. Using SHAP (SHapley Additive exPlanation) values has proved to be a useful tool to identify significant features. In this paper, we propose implementing Kernel SHAP in the shap-select module to extend its functionality to non-tree-based models. This is done by introducing a masker that generates background values that SHAP uses to simulate the removal of a feature. The performance is then evaluated on a small high-dimensional dataset, and the resulting feature selection is evaluated. The results show that, despite higher instability in the performance metrics, the method’s capacity to identify significant features remains promising. These findings can serve as a starting point for further research into the relationship between feature characteristics and feature selection methods, as well as for improving the capacity to identify relevant features using SHAP values for feature selection.