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

arXiv: https://arxiv.org/abs/2106.07827 · HTML: https://mlsanity.com/paper/2106.07827/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach](https://mlsanity.com/paper/1606.09066/) (2016)

## Built on by (most cited first, 3 of 3)

- [Personalized and Reliable Decision Sets: Enhancing Interpretability in Clinical Decision Support Systems](https://mlsanity.com/paper/2107.07483/) (2021)
- [Enhancing Medical Learning and Reasoning Systems: A Boxology-Based Comparative Analysis of Design Patterns](https://mlsanity.com/paper/2408.02709/) (2024)
- [Intervention Efficiency and Perturbation Validation Framework: Capacity-Aware and Robust Clinical Model Selection under the Rashomon Effect](https://mlsanity.com/paper/2511.14317/) (2025)

## Abstract (arXiv)

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.