## 1 Introduction
They subsequently used traditional classifiers for the prediction and diagnosis of heart disease, obtaining a classification percentage of 99.2% using the decision tree technique and 96.5% using the naive Bayes technique. [PERSON] et al. [13] presented a classification model of heart disease using the Statlog data set. This is composed of two systems: the first system uses the Relief algorithm to extract the superior characteristics, discarding the features that offer less information, then the Rough Set theory (RS) reduction heuristic for feature elimination. Then the new data set is trained with the reduced features to perform tests with the different traditional classifiers, obtaining better results using the C4.5 technique: a classification percentage of 92.59% with the seven features that presented the strongest qualities in distinction. Efforts have been made to comply with the regulatory framework proposed by the European Commission [14] as a strategy for the future, which is based on building confidence in artificial intelligence and how it focuses on the human being. The reliability of these systems will depend on three main components: it must be in accordance with the law, it must respect the basic principles, and it must be solid. Similarly, and complying with the requirements derived from AI's necessary components set out above (i.e., human intervention and supervision, technical soundness and security, privacy and data management, transparency, diversity, non-discrimination and equity, social and environmental welfare, and accountability), our proposed methodology addresses the known concerns, specifically in Europe, where they are concerned that the results of AI are not reliable. Our method focuses on the strength, safety, and transparency of techniques, meaning the decisions must be correct and reflect the correct reproducibility of the results from the documentation of the decisions taken, in addition to allowing the interpretation of a system with few related features as an explanatory mechanism in decision making. In the field of healthcare, the problem of interpretability plays a crucial role, since it is not possible to make a decision if it cannot be directly described in understandable terms. The doctor cannot trust a decision that they cannot explain, and the patient will not be able to trust an expert who bases their decisions on the result of a computational method. For this reason, some lines of research have sought simpler and more interpretable models, such as semantic representations [15; 16] or attention mechanisms [17; 18; 19]. However, medical reasoning is compatible with rule-based representations [20] and, in this sense, one of the most interpretable models is the decision tree [21; 22]. This research arose from an interest in investigating the multi-objective nature of the problem of improving the accuracy of classification versus the interpretability of a model obtained. We focused on the estimation of cardiovascular diseases. The results will assist cardiologists to enrich the quality of life of patients who may or may not suffer from cardiovascular diseases by examining and verifying the results obtained in an agile and efficient manner, generating knowledge and expectations for doctors about how to increase the precision of diagnoses prediction, to help control or prevent the risk factors of CVDs using an interpretable system. For this purpose, data from the free repository of the University of California (UCI) were used [23], more specifically from the Statlog Heart Disease data set, since it is the most used and has the most articles about the application of machine learning (ML) techniques, which enables the classification of any type of cardiac anomaly from the analysis of this data set. The interpretation of the results after applying ML techniques to a data set with many features is often complicated, although the techniques used are theoretically interpretable.