Title: Deep learning algorithm based pregnant women heart disease prediction using heart beats

Authors: S. Magesh; P.S. Rajakumar

Addresses: Department of Computer Science and Engineering, Dr. M.G.R. Educational and Research Institute, Chennai, 600095, Tamil Nadu, India ' Department of Computer Science and Engineering, Dr. M.G.R. Educational and Research Institute, Chennai, 600095, Tamil Nadu, India

Abstract: The main objective of this study is to acquaint participants with the procedure for diagnosing maternal cardiac disease and the function of a multidisciplinary team in its management. Heart failure, arrhythmias, and a worsening of the heart condition can all complicate pregnancy and raise the risk of morbidity and mortality for both the mother and the foetus. The doctor may now provide both normal and specialised care during pregnancy thanks to the availability of specialised guidelines on pregnancy and heart disease in the last 10 years. Rely only on deep learning (DL) for screening pregnant women in the second trimester for congenital heart disease (CHD) and foetal ectasia. This ground breaking research not only targets FE in the first trimester but also develops a revolutionary information system platform with a number of tools for interpreting common ultrasound data, making it innovative and groundbreaking. This gives the project its uniqueness and ingenuity.

Keywords: deep learning; foetal cardiology; echocardiography; decision support systems; congenital heart disease; foetal ectasia; heart disease; pregnant; ultrasound.

DOI: 10.1504/IJSSE.2026.156331

International Journal of System of Systems Engineering, 2026 Vol.16 No.4, pp.514 - 530

Received: 16 Jan 2024
Accepted: 22 May 2024
Published online: 13 Sep 2026 *

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