Intrinsically Interpretable Decision Trees for Healthcare Applications

Abstract The deployment of machine learning for high-stakes decision support may demand algorithms that are intrinsically interpretable so that a model can be interrogated by a user to understand how the model arrived at an output given an input. Decision trees exemplify intrinsically interpretable machine learning but to achieve acceptable performance they rely on ensemble methods which sacrifice their interpretability because there is no single, or ‘canonical’ tree for the user to inspect. Sequential Monte Carlo Decision Trees are a novel method for training populations of decision trees that do not rely on ensembles or subspace sampling and achieve comparable performance to other tree-based methods. With SMC-DTs, each of the population of trees are trained on the entire feature space and each is a candidate for an intrinsically interpretable canonical decision tree. In this paper, we propose a tree-similarity algorithm to assist a user in choosing a final, canonical high-performing tree that is consistent (in structure) with other high-performing candidate trees. This enables the user to reduce the pool of candidates to a small number that can be inspected to select a final tree for deployment. We show that SMC-DTs are competitive with well-established methods Random Forest, Extreme Gradient Boosting Trees and Classification and Regression Trees on four benchmark health-related data sets. We then demonstrate the combination of SMC-DTs with the tree similarity method to derive and present an interpretable decision tree to identify university students likely to experience suicidal ideation using summary and administrative data university welfare services would be able to access for their students (i.e. without detailed clinical information). We conclude that combining SMC-DTs with tree-similarity offers a plausible way of delivering intrinsic interpretability for use in mixed-data type tabular data sets.