Title: A holistic approach to early congenital heart disease detection in rural neonates: bridging the gap in postnatal care

Authors: Rishika Anand; S.R.N. Reddy; Dinesh Kumar Yadav

Addresses: Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, India ' Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, India ' Department of Pediatric, Ram Manohar Lohia Hospital, Delhi, India

Abstract: This research seeks to combat the alarming neonatal mortality rates attributed to undiagnosed congenital heart disease (CHD) in underserved rural regions, which often lack the necessary medical equipment and treatments. To address this pressing issue, we propose the development of an early detection system capable of identifying CHD in infants, thus facilitating timely intervention and enhancing postnatal care. Our study advocates an integrated approach that comprehensively analyses various vital parameters, including blood pressure, ECG, SpO2, body temperature, and heart rate in newborns, ensuring the accurate detection of CHD. The primary objective is to enhance the support and guidance provided to families in rural areas concerning postnatal care, prognosis, and treatment strategies. To gauge the effectiveness of our approach, we will compare it with existing techniques and evaluate its precision. The outcomes of this research hold the potential to significantly diminish neonatal mortality rates in these underserved regions.

Keywords: machine learning; deep learning; congenital heart disease; CHD; early detection.

DOI: 10.1504/IJBIC.2024.140122

International Journal of Bio-Inspired Computation, 2024 Vol.24 No.1, pp.42 - 54

Received: 01 Jan 2023
Accepted: 08 Dec 2023
Published online: 24 Jul 2024 *

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