  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.
