ItemArticle

A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing

Title
A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing
Creator
Pachiyannan P.; Alsulami M.; Alsadie D.; Saudagar A.K.J.; AlKhathami M.; Poonia R.C.
Description
Congenital heart disease (CHD) represents a multifaceted medical condition that requires early detection and diagnosis for effective management, given its diverse presentations and subtle symptoms that manifest from birth. This research article introduces a groundbreaking healthcare application, the Machine Learning-based Congenital Heart Disease Prediction Method (ML-CHDPM), tailored to address these challenges and expedite the timely identification and classification of CHD in pregnant women. The ML-CHDPM model leverages state-of-the-art machine learning techniques to categorize CHD cases, taking into account pertinent clinical and demographic factors. Trained on a comprehensive dataset, the model captures intricate patterns and relationships, resulting in precise predictions and classifications. The evaluation of the models performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPMs superiority across six pivotal metrics: accuracy, precision, recall, specificity, false positive rate (FPR), and false negative rate (FNR). The method achieves an average accuracy rate of 94.28%, precision of 87.54%, recall rate of 96.25%, specificity rate of 91.74%, FPR of 8.26%, and FNR of 3.75%. These outcomes distinctly demonstrate the ML-CHDPMs effectiveness in reliably predicting and classifying CHD cases. This research marks a significant stride toward early detection and diagnosis, harnessing advanced machine learning techniques within the realm of ECG signal processing, specifically tailored to pregnant women. 2024 by the authors.
Source
Technologies, Vol-12, No. 1
Date
2024-01-01
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Subject
classification; congenital heart disease; healthcare; internet of medical things; prediction
Coverage
Pachiyannan P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Alsulami M., Department of Software Engineering, Umm Al-Qura University, Makkah, 21961, Saudi Arabia; Alsadie D., Department of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah, 21961, Saudi Arabia; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; AlKhathami M., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Delhi-NCR, 201003, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 22277080
Format
Online
Language
English
Type
Article

Collection

Articles

Citation

Pachiyannan P.; Alsulami M.; Alsadie D.; Saudagar A.K.J.; AlKhathami M.; Poonia R.C., “A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/13737.

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