Advanced machine learning techniques for cardiovascular disease early detection and diagnosis
Abstract The identification and prognosis of the potential for developing Cardiovascular Diseases (CVD) in healthy individuals is a vital aspect of disease management. Accessing the comprehensive health data on CVD currently available within hospital databases holds significant potential for the ear...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-09-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00817-1 |
| _version_ | 1797452320502448128 |
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| author | Nadiah A. Baghdadi Sally Mohammed Farghaly Abdelaliem Amer Malki Ibrahim Gad Ashraf Ewis Elsayed Atlam |
| author_facet | Nadiah A. Baghdadi Sally Mohammed Farghaly Abdelaliem Amer Malki Ibrahim Gad Ashraf Ewis Elsayed Atlam |
| author_sort | Nadiah A. Baghdadi |
| collection | DOAJ |
| description | Abstract The identification and prognosis of the potential for developing Cardiovascular Diseases (CVD) in healthy individuals is a vital aspect of disease management. Accessing the comprehensive health data on CVD currently available within hospital databases holds significant potential for the early detection and diagnosis of CVD, thereby positively impacting disease outcomes. Therefore, the incorporation of machine learning methods holds significant promise in the advancement of clinical practice for the management of Cardiovascular Diseases (CVDs). By providing a means to develop evidence-based clinical guidelines and management algorithms, these techniques can eliminate the need for costly and extensive clinical and laboratory investigations, reducing the associated financial burden on patients and the healthcare system. In order to optimize early prediction and intervention for CVDs, this study proposes the development of novel, robust, effective, and efficient machine learning algorithms, specifically designed for the automatic selection of key features and the detection of early-stage heart disease. The proposed Catboost model yields an F1-score of about 92.3% and an average accuracy of 90.94%. Therefore, Compared to many other existing state-of-art approaches, it successfully achieved and maximized classification performance with higher percentages of accuracy and precision. |
| first_indexed | 2024-03-09T15:06:52Z |
| format | Article |
| id | doaj.art-8f9d597a7a1b4c328d0c44d6fae08ca1 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-09T15:06:52Z |
| publishDate | 2023-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-8f9d597a7a1b4c328d0c44d6fae08ca12023-11-26T13:35:32ZengSpringerOpenJournal of Big Data2196-11152023-09-0110112910.1186/s40537-023-00817-1Advanced machine learning techniques for cardiovascular disease early detection and diagnosisNadiah A. Baghdadi0Sally Mohammed Farghaly Abdelaliem1Amer Malki2Ibrahim Gad3Ashraf Ewis4Elsayed Atlam5Nursing Management and Education Department, College of Nursing, Princess Nourah bint Abdulrahman UniversityNursing Management and Education Department, College of Nursing, Princess Nourah bint Abdulrahman UniversityComputer Science Section, College of Computer Science and Engineering, Taibah University, Yanbu CampusComputer Science Department, Faculty of Science, Tanta UniversityDepartment of Public Health and Occupational Medicine, Faculty of Medicine, Minia UniversityComputer Science Section, College of Computer Science and Engineering, Taibah University, Yanbu CampusAbstract The identification and prognosis of the potential for developing Cardiovascular Diseases (CVD) in healthy individuals is a vital aspect of disease management. Accessing the comprehensive health data on CVD currently available within hospital databases holds significant potential for the early detection and diagnosis of CVD, thereby positively impacting disease outcomes. Therefore, the incorporation of machine learning methods holds significant promise in the advancement of clinical practice for the management of Cardiovascular Diseases (CVDs). By providing a means to develop evidence-based clinical guidelines and management algorithms, these techniques can eliminate the need for costly and extensive clinical and laboratory investigations, reducing the associated financial burden on patients and the healthcare system. In order to optimize early prediction and intervention for CVDs, this study proposes the development of novel, robust, effective, and efficient machine learning algorithms, specifically designed for the automatic selection of key features and the detection of early-stage heart disease. The proposed Catboost model yields an F1-score of about 92.3% and an average accuracy of 90.94%. Therefore, Compared to many other existing state-of-art approaches, it successfully achieved and maximized classification performance with higher percentages of accuracy and precision.https://doi.org/10.1186/s40537-023-00817-1Heart diseaseMachine learningFeature selectionCardiovascular diseasesQuality of lifeDisease prevention |
| spellingShingle | Nadiah A. Baghdadi Sally Mohammed Farghaly Abdelaliem Amer Malki Ibrahim Gad Ashraf Ewis Elsayed Atlam Advanced machine learning techniques for cardiovascular disease early detection and diagnosis Journal of Big Data Heart disease Machine learning Feature selection Cardiovascular diseases Quality of life Disease prevention |
| title | Advanced machine learning techniques for cardiovascular disease early detection and diagnosis |
| title_full | Advanced machine learning techniques for cardiovascular disease early detection and diagnosis |
| title_fullStr | Advanced machine learning techniques for cardiovascular disease early detection and diagnosis |
| title_full_unstemmed | Advanced machine learning techniques for cardiovascular disease early detection and diagnosis |
| title_short | Advanced machine learning techniques for cardiovascular disease early detection and diagnosis |
| title_sort | advanced machine learning techniques for cardiovascular disease early detection and diagnosis |
| topic | Heart disease Machine learning Feature selection Cardiovascular diseases Quality of life Disease prevention |
| url | https://doi.org/10.1186/s40537-023-00817-1 |
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