Title: Neonatal heart disease screening using an ensemble of decision trees

Authors: Amir M. Amiri; Giuliano Armano; Seyedhossein Ghasemi

Addresses: Department of Biomedical Engineering, Widener University, Chester, USA ' Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy ' Department of Software Engineering, Azad University, Birjand, Iran

Abstract: This paper is concerned with the occurrence of a heart disease specifically for the neonate, as those seriously affected may face an increased risk of death. In this paper, a novel computer-based tool is proposed for a medical centre diagnosis aimed at monitoring neonates who are potential vulnerable to heart disease. In particular, cardiac cycles of phonocardiograms (PCGs) are first pre-processed and then used to train an ensemble of decision trees (DTs). The classifier model consists of 12 trees, with bagging and hold-out methods used for training and testing. Several feature encoding methods have been experimented with to generate the feature space over which the classifier has been tested, including Shannon energy and Wigner bispectrum. On average 93.91% classification accuracy, 96.15% sensitivity and 91.67% specificity have been obtained from the given data, which has been validated with a balanced dataset of 110 PCG signals taken from healthy and unhealthy medical cases.

Keywords: neonate; heart diseases; phonocardiogram; ensemble of decision trees; ventricular septal defect; machine learning; heart murmurs; time-frequency features; decision trees.

DOI: 10.1504/IJBET.2022.124014

International Journal of Biomedical Engineering and Technology, 2022 Vol.39 No.2, pp.107 - 130

Received: 07 Jan 2019
Accepted: 04 Apr 2019
Published online: 11 Jul 2022 *

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