Interpretable Hybrid Model in the Identification of Heart Sounds
Sprache des Titels:
Heart problems are primarily responsible for the deaths of countless people worldwide. The use of intelligent techniques to assist in the identification of existing patterns in these diseases can facilitate treatments and decision making in the field of medicine. This work aims to extract knowledge from a dataset based on heart noise behaviors in order to determine whether heart murmur predilection exists or not in the analyzed patients. A heart murmur can be pathological due to defects in the heart, so the use of an evolving hybrid technique can assist in detecting this comorbidity team and, at the same time, extract knowledge through fuzzy linguistic rules, facilitating the understanding of the nature of the evaluated data. Heart disease detection tests were performed
to compare the proposed hybrid model?s performance with state of the art for the subject. The
results obtained (90.75% of accuracy) prove that in addition to great assertiveness in detecting heart
murmurs, the evolving hybrid model could fay it, concomitant with the extraction of knowledge
from data submitted to an intelligent approach.