Improving the compromise between accuracy, interpretability and personalization of rule-based machine learning in medical problems

TL;DR

This paper introduces a personalized decision rule prediction component that enhances the balance between accuracy and interpretability in clinical machine learning models, leading to improved predictive performance.

Contribution

It proposes a novel method to predict rule correctness for individual patients, adding personalization and boosting performance in rule-based models.

Findings

Improved predictive accuracy on clinical datasets.

Enhanced interpretability with personalized rule assessment.

Better trade-off management between accuracy and simplicity.

Abstract

One of the key challenges when developing a predictive model is the capability to describe the domain knowledge and the cause-effect relationships in a simple way. Decision rules are a useful and important methodology in this context, justifying their application in several areas, particularly in clinical practice. Several machine-learning classifiers have exploited the advantageous properties of decision rules to build intelligent prediction models, namely decision trees and ensembles of trees (ETs). However, such methodologies usually suffer from a trade-off between interpretability and predictive performance. Some procedures consider a simplification of ETs, using heuristic approaches to select an optimal reduced set of decision rules. In this paper, we introduce a novel step to those methodologies. We create a new component to predict if a given rule will be correct or not for a particular patient, which introduces personalization into the procedure. Furthermore, the validation results using three public clinical datasets suggest that it also allows to increase the predictive performance of the selected set of rules, improving the mentioned trade-off.