Enhancing the Interpretability of Cardiovascular Disease Classifiers using Born-Again Tree Ensembles

• L. G. S. N. A. Magalhães ENCE-IBGE
• E. C. Gonçalves ENCE-IBGE

DOI: https://doi.org/10.5753/kdmile.2024.243749

Resumo

According to the Pan American Health Organization, more people die each year from cardiovascular diseases than from any other cause. Due to this, ensemble classifiers such as Random Forest (RF) have been increasingly employed to build models targeted at the early prediction of such diseases. Nevertheless, one of the main disadvantages of the ensemble approaches lies in the fact that they cannot be applied when the goal is to build interpretable models (which are often desired or even required by both physicians and patients). To tackle this problem, in this work we evaluate the use of Born-Again Tree Ensembles (BA), a recently proposed technique that corresponds to the the first exact algorithm that transforms an RF into a single decision tree. Experiments carried out on a dataset containing data about 1,417 patients show that BA was able to produce a model that is directly interpretable, while at the same time keeping the same predictive power of an RF model.

Palavras-chave: born-again tree, random forest, interpretability, cardiovascular disease classification, data mining

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