Machine learning-based identification of patients with a cardiovascular defect
Abstract Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early identification is a vital step in rehabilitation and treatment. T...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2021-10-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-021-00524-9 |
| _version_ | 1818890279703281664 |
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| author | Nabaouia Louridi Samira Douzi Bouabid El Ouahidi |
| author_facet | Nabaouia Louridi Samira Douzi Bouabid El Ouahidi |
| author_sort | Nabaouia Louridi |
| collection | DOAJ |
| description | Abstract Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early identification is a vital step in rehabilitation and treatment. To diagnose heart defects, it would be necessary to implement a system able to predict the existence of heart diseases. In the current article, our main motivation is to develop an effective intelligent medical system based on machine learning techniques, to aid in identifying a patient’s heart condition and guide a doctor in making an accurate diagnosis of whether or not a patient has cardiovascular diseases. Using multiple data processing techniques, we address the problem of missing data as well as the problem of imbalanced data in the publicly available UCI Heart Disease dataset and the Framingham dataset. Furthermore, we use machine learning to select the most effective algorithm for predicting cardiovascular diseases. Different metrics, such as accuracy, sensitivity, F-measure, and precision, were used to test our system, demonstrating that the proposed approach significantly outperforms other models. |
| first_indexed | 2024-12-19T17:22:24Z |
| format | Article |
| id | doaj.art-868d8a3f5c7f49859ed1f658f6f1433c |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-19T17:22:24Z |
| publishDate | 2021-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-868d8a3f5c7f49859ed1f658f6f1433c2022-12-21T20:12:38ZengSpringerOpenJournal of Big Data2196-11152021-10-018111510.1186/s40537-021-00524-9Machine learning-based identification of patients with a cardiovascular defectNabaouia Louridi0Samira Douzi1Bouabid El Ouahidi2Department of Computer LRI, Faculty of Sciences Mohammed VDepartment of Computer LRI, Faculty of Sciences Mohammed VDepartment of Computer LRI, Faculty of Sciences Mohammed VAbstract Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early identification is a vital step in rehabilitation and treatment. To diagnose heart defects, it would be necessary to implement a system able to predict the existence of heart diseases. In the current article, our main motivation is to develop an effective intelligent medical system based on machine learning techniques, to aid in identifying a patient’s heart condition and guide a doctor in making an accurate diagnosis of whether or not a patient has cardiovascular diseases. Using multiple data processing techniques, we address the problem of missing data as well as the problem of imbalanced data in the publicly available UCI Heart Disease dataset and the Framingham dataset. Furthermore, we use machine learning to select the most effective algorithm for predicting cardiovascular diseases. Different metrics, such as accuracy, sensitivity, F-measure, and precision, were used to test our system, demonstrating that the proposed approach significantly outperforms other models.https://doi.org/10.1186/s40537-021-00524-9Cardiovascular diseasesData imputationMachine learningPreprocessingNormalization |
| spellingShingle | Nabaouia Louridi Samira Douzi Bouabid El Ouahidi Machine learning-based identification of patients with a cardiovascular defect Journal of Big Data Cardiovascular diseases Data imputation Machine learning Preprocessing Normalization |
| title | Machine learning-based identification of patients with a cardiovascular defect |
| title_full | Machine learning-based identification of patients with a cardiovascular defect |
| title_fullStr | Machine learning-based identification of patients with a cardiovascular defect |
| title_full_unstemmed | Machine learning-based identification of patients with a cardiovascular defect |
| title_short | Machine learning-based identification of patients with a cardiovascular defect |
| title_sort | machine learning based identification of patients with a cardiovascular defect |
| topic | Cardiovascular diseases Data imputation Machine learning Preprocessing Normalization |
| url | https://doi.org/10.1186/s40537-021-00524-9 |
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