DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features
One of the leading causes of mortality worldwide is cardiovascular disease (CVD). Electrocardiography (ECG) is a noninvasive and cost-effective tool to diagnose the heart’s health. This study presents a multi-class classifier for the prediction of four different types of Cardiovascular Di...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10287352/ |
| _version_ | 1797644881733091328 |
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| author | Nidhi Sinha M. A. Ganesh Kumar Amit M. Joshi Linga Reddy Cenkeramaddi |
| author_facet | Nidhi Sinha M. A. Ganesh Kumar Amit M. Joshi Linga Reddy Cenkeramaddi |
| author_sort | Nidhi Sinha |
| collection | DOAJ |
| description | One of the leading causes of mortality worldwide is cardiovascular disease (CVD). Electrocardiography (ECG) is a noninvasive and cost-effective tool to diagnose the heart’s health. This study presents a multi-class classifier for the prediction of four different types of Cardiovascular Diseases, i.e., Myocardial Infarction, Hypertrophy, Conduction Disturbances, and ST-T abnormality using 12-lead ECG. There are four key steps involved in the presented work: data preprocessing, feature extraction, data preparation, and augmentation, and modelling for multi-class CVD classification. The sixteen-time domain augmented features are used to train the classifier. The work is divided into three parts: extracting the features from raw 12-lead ECG signals, data preparation and augmentation, and training, testing, and validating the classifier. A comparative study of the performance of five different classifiers (i.e., Random Forest (RF), K Nearest Neighbors (KNN), Gradient Boost, Adda Boost, and XG Boost has also been presented. Accuracy, precision, recall, and F1 scores are used for performance evaluation. Further, the Receiver Operating Curve (ROC) is traced, and the Area Under the Curve (AUC) is calculated to ensure the unbiased performance of the classifier. The application of the proposed classifier in the Smart Healthcare framework has also been discussed. |
| first_indexed | 2024-03-11T14:38:49Z |
| format | Article |
| id | doaj.art-34ddb9bf6b774810881b27b86f134d44 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T14:38:49Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-34ddb9bf6b774810881b27b86f134d442023-10-30T23:00:19ZengIEEEIEEE Access2169-35362023-01-011111764311765510.1109/ACCESS.2023.332570510287352DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series FeaturesNidhi Sinha0M. A. Ganesh Kumar1Amit M. Joshi2https://orcid.org/0000-0001-7919-1652Linga Reddy Cenkeramaddi3https://orcid.org/0000-0002-1023-2118Department of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, IndiaDepartment of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, IndiaDepartment of Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, IndiaDepartment of Information and Communication Technology, University of Agder, Grimstad, NorwayOne of the leading causes of mortality worldwide is cardiovascular disease (CVD). Electrocardiography (ECG) is a noninvasive and cost-effective tool to diagnose the heart’s health. This study presents a multi-class classifier for the prediction of four different types of Cardiovascular Diseases, i.e., Myocardial Infarction, Hypertrophy, Conduction Disturbances, and ST-T abnormality using 12-lead ECG. There are four key steps involved in the presented work: data preprocessing, feature extraction, data preparation, and augmentation, and modelling for multi-class CVD classification. The sixteen-time domain augmented features are used to train the classifier. The work is divided into three parts: extracting the features from raw 12-lead ECG signals, data preparation and augmentation, and training, testing, and validating the classifier. A comparative study of the performance of five different classifiers (i.e., Random Forest (RF), K Nearest Neighbors (KNN), Gradient Boost, Adda Boost, and XG Boost has also been presented. Accuracy, precision, recall, and F1 scores are used for performance evaluation. Further, the Receiver Operating Curve (ROC) is traced, and the Area Under the Curve (AUC) is calculated to ensure the unbiased performance of the classifier. The application of the proposed classifier in the Smart Healthcare framework has also been discussed.https://ieeexplore.ieee.org/document/10287352/Cardiovascular disease (CVD)PTB-XL datamachine learningsmart healthcareECGheart failure |
| spellingShingle | Nidhi Sinha M. A. Ganesh Kumar Amit M. Joshi Linga Reddy Cenkeramaddi DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features IEEE Access Cardiovascular disease (CVD) PTB-XL data machine learning smart healthcare ECG heart failure |
| title | DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features |
| title_full | DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features |
| title_fullStr | DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features |
| title_full_unstemmed | DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features |
| title_short | DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features |
| title_sort | dasmcc data augmented smote multi class classifier for prediction of cardiovascular diseases using time series features |
| topic | Cardiovascular disease (CVD) PTB-XL data machine learning smart healthcare ECG heart failure |
| url | https://ieeexplore.ieee.org/document/10287352/ |
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