A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/12/1/4 |
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| author | Prabu Pachiyannan Musleh Alsulami Deafallah Alsadie Abdul Khader Jilani Saudagar Mohammed AlKhathami Ramesh Chandra Poonia |
| author_facet | Prabu Pachiyannan Musleh Alsulami Deafallah Alsadie Abdul Khader Jilani Saudagar Mohammed AlKhathami Ramesh Chandra Poonia |
| author_sort | Prabu Pachiyannan |
| collection | DOAJ |
| 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 model’s performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM’s 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-CHDPM’s 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. |
| first_indexed | 2024-03-08T10:34:31Z |
| format | Article |
| id | doaj.art-876ff4eaaf734d6a80e1be6de6e89dfd |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-08T10:34:31Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-876ff4eaaf734d6a80e1be6de6e89dfd2024-01-26T18:40:04ZengMDPI AGTechnologies2227-70802024-01-01121410.3390/technologies12010004A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal ProcessingPrabu Pachiyannan0Musleh Alsulami1Deafallah Alsadie2Abdul Khader Jilani Saudagar3Mohammed AlKhathami4Ramesh Chandra Poonia5Department of Computer Science, CHRIST (Deemed to be University), Bangalore 560029, IndiaDepartment of Software Engineering, Umm Al-Qura University, Makkah 21961, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah 21961, Saudi ArabiaInformation Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaInformation Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaDepartment of Computer Science, CHRIST (Deemed to be University), Delhi-NCR 201003, IndiaCongenital 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 model’s performance encompasses sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. Remarkably, the findings underscore the ML-CHDPM’s 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-CHDPM’s 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.https://www.mdpi.com/2227-7080/12/1/4healthcareinternet of medical thingscongenital heart diseaseclassificationprediction |
| spellingShingle | Prabu Pachiyannan Musleh Alsulami Deafallah Alsadie Abdul Khader Jilani Saudagar Mohammed AlKhathami Ramesh Chandra Poonia A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing Technologies healthcare internet of medical things congenital heart disease classification prediction |
| title | A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing |
| title_full | A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing |
| title_fullStr | A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing |
| title_full_unstemmed | A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing |
| title_short | A Novel Machine Learning-Based Prediction Method for Early Detection and Diagnosis of Congenital Heart Disease Using ECG Signal Processing |
| title_sort | novel machine learning based prediction method for early detection and diagnosis of congenital heart disease using ecg signal processing |
| topic | healthcare internet of medical things congenital heart disease classification prediction |
| url | https://www.mdpi.com/2227-7080/12/1/4 |
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