Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing Subject The topic of the resource classification; congenital heart disease; healthcare; internet of medical things; prediction Description An account of the resource 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. Creator An entity primarily responsible for making the resource Pachiyannan P.; Alsulami M.; Alsadie D.; Saudagar A.K.J.; AlKhathami M.; Poonia R.C. Source A related resource from which the described resource is derived Technologies, Vol-12, No. 1 Publisher An entity responsible for making the resource available Multidisciplinary Digital Publishing Institute (MDPI) Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/technologies12010004" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/technologies12010004</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183410549&amp;doi=10.3390%2Ftechnologies12010004&amp;partnerID=40&amp;md5=72e38b95cc5ee341a8eaa309ecd216ab" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183410549&amp;doi=10.3390%2ftechnologies12010004&amp;partnerID=40&amp;md5=72e38b95cc5ee341a8eaa309ecd216ab</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 22277080 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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