Sistema inteligente para identificação de risco de morte súbita em pacientes chagásicos utilizando modelos híbridos

Authors

  • Bruno M. P. Takazono Instituto Federal de Educação e Tecnologia do Ceará (IFCE), CE
  • Isabelly P. da Costa Instituto Federal de Educação e Tecnologia do Ceará (IFCE), CE
  • Jose L. dos Santos Neto Instituto Federal de Educação e Tecnologia do Ceará (IFCE), CE
  • Victor M. de Souza Instituto Federal de Educação e Tecnologia do Ceará (IFCE), CE
  • João P. V. Madeiro Universidade Feferal do Ceará (UFC), CE
  • Roberto C. Pedrosa Instituto do Coração Edson Saad – Universidade Federal do Rio de Janeiro (UFRJ), RJ
  • Carlos H. L. Cavalcante Instituto Federal de Educação e Tecnologia do Ceará (IFCE), CE

Keywords:

Chagas disease, Sudden Cardiac Death, Machine Learning, Recurrent Neural Network

Abstract

As a leading cause of death in Chagas disease, affecting an estimated 6 to 7 million people worldwide, sudden cardiac death (SCD) in Chagasic cardiomyopathy (CC) remains a significant challenge. This study proposes the development of am intelligent web system prototype that utilizes machine learning algorithms to predict SCD risk in low-risk CC patients. Consequently, an intelligent web system prototype was developed that employs neural networks as a prediction model, achieving an accuracy of 94.88% and a sensitivity of 82.3%. This work aims to contribute to the diagnosis of cardiac risk in low-risk CC patients, providing a valuable tool for use in countries with endemic prevalence of Chagas disease.

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Published

2024-10-18

Issue

Vol. 4 No. 1 (2024): CBA2024

Section

Articles