Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques
Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry’s c...
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| Published in | Computational intelligence and neuroscience Vol. 2022; pp. 1 - 13 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Hindawi
11.01.2022
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-5265 1687-5273 1687-5273 |
| DOI | 10.1155/2022/2973324 |
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| Abstract | Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry’s clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS’s performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds. |
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| AbstractList | Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry's clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS's performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds. Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry's clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS's performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds.Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry's clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS's performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds. |
| Audience | Academic |
| Author | Nadakinamani, Rajkumar Gangappa Kautish, Sandeep Mohamed, Ali Wagdy Gupta, Yogita Abdelwahab, Sayed F. Vibith, A. S. Reyana, A. |
| AuthorAffiliation | 2 Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India 3 Department of Computer Science and Engineering, LBEF Campus, Kathmandu, Nepal, India 5 Department of Biotechnology, Thapar Institute of Engineering & Technology, Patiala, India 1 Badr Al Samaa Hospital, Muscat, Oman 7 Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt 6 Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, PO Box 11099, Taif 21944, Saudi Arabia 4 Department of Computer Science and Engineering, RMK College of Engineering and Technology, Tiruvallur, Tamil Nadu, India 8 Department of Mathematics and Actuarial Science, School of Science and Engineering, The American University in Cairo, New Cairo, Egypt |
| AuthorAffiliation_xml | – name: 4 Department of Computer Science and Engineering, RMK College of Engineering and Technology, Tiruvallur, Tamil Nadu, India – name: 3 Department of Computer Science and Engineering, LBEF Campus, Kathmandu, Nepal, India – name: 7 Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt – name: 2 Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India – name: 8 Department of Mathematics and Actuarial Science, School of Science and Engineering, The American University in Cairo, New Cairo, Egypt – name: 1 Badr Al Samaa Hospital, Muscat, Oman – name: 5 Department of Biotechnology, Thapar Institute of Engineering & Technology, Patiala, India – name: 6 Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, PO Box 11099, Taif 21944, Saudi Arabia |
| Author_xml | – sequence: 1 givenname: Rajkumar Gangappa surname: Nadakinamani fullname: Nadakinamani, Rajkumar Gangappa organization: Badr Al Samaa HospitalMuscatOman – sequence: 2 givenname: A. orcidid: 0000-0001-8429-4242 surname: Reyana fullname: Reyana, A. organization: Department of Computer Science and EngineeringHindusthan College of Engineering and TechnologyCoimbatoreTamil NaduIndiahindusthan.net – sequence: 3 givenname: Sandeep orcidid: 0000-0001-5120-5741 surname: Kautish fullname: Kautish, Sandeep organization: Department of Computer Science and EngineeringLBEF CampusKathmanduNepalIndia – sequence: 4 givenname: A. S. surname: Vibith fullname: Vibith, A. S. organization: Department of Computer Science and EngineeringRMK College of Engineering and TechnologyTiruvallurTamil NaduIndia – sequence: 5 givenname: Yogita surname: Gupta fullname: Gupta, Yogita organization: Department of BiotechnologyThapar Institute of Engineering & TechnologyPatialaIndiathapar.edu – sequence: 6 givenname: Sayed F. orcidid: 0000-0002-9636-7485 surname: Abdelwahab fullname: Abdelwahab, Sayed F. organization: Department of Pharmaceutics and Industrial PharmacyCollege of PharmacyTaif UniversityPO Box 11099Taif 21944Saudi Arabiatu.edu.sa – sequence: 7 givenname: Ali Wagdy orcidid: 0000-0002-5895-2632 surname: Mohamed fullname: Mohamed, Ali Wagdy organization: Operations Research DepartmentFaculty of Graduate Studies for Statistical ResearchCairo UniversityGiza 12613Egyptcu.edu.eg |
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| CitedBy_id | crossref_primary_10_1155_2023_9815067 crossref_primary_10_1186_s42492_023_00143_6 crossref_primary_10_1016_j_jbi_2023_104577 crossref_primary_10_20473_jisebi_9_2_119_135 crossref_primary_10_29121_shodhkosh_v5_i3_2024_3336 crossref_primary_10_1109_ACCESS_2024_3373646 crossref_primary_10_1371_journal_pone_0293759 crossref_primary_10_1007_s00704_023_04589_9 crossref_primary_10_3390_diagnostics13122071 crossref_primary_10_3390_app122110787 crossref_primary_10_1038_s41598_024_53410_8 crossref_primary_10_31642_JoKMC_2018_100104 crossref_primary_10_1016_j_drudis_2024_104280 crossref_primary_10_1038_s41598_023_40717_1 crossref_primary_10_3390_pr11030734 crossref_primary_10_1142_S0219519423300016 crossref_primary_10_1155_2023_1406060 crossref_primary_10_3390_s22197227 crossref_primary_10_1007_s11042_022_14305_w crossref_primary_10_1002_ece3_70736 crossref_primary_10_3390_math11030707 crossref_primary_10_35940_ijeat_C3984_0212323 crossref_primary_10_33317_ssurj_649 crossref_primary_10_32604_csse_2023_042294 crossref_primary_10_1109_ACCESS_2023_3322943 crossref_primary_10_1016_j_mtbio_2025_101663 |
| Cites_doi | 10.1007/s40815-018-0559-3 10.1177/2047487320916823 10.1186/s12911-019-0918-5 10.1155/2018/3860146 10.1093/eurheartj/ehy404 10.1371/journal.pone.0174944 10.1016/j.imu.2019.100203 10.1016/j.jare.2020.04.006 10.1002/acs.2967 10.1007/s00395-020-0792-4 10.1016/j.eij.2011.04.003 10.1038/s41598-020-72685-1 10.1007/s12553-020-00438-1 10.1038/s41597-019-0206-3 10.1088/1742-6596/1817/1/012009 10.1136/bmj.m3919 10.22266/ijies2019.0228.24 10.1007/s13755-019-0095-z 10.1186/s12968-019-0575-y 10.1109/access.2020.3015757 10.1016/j.envpol.2020.114630 10.1371/journal.pone.0213653 10.1161/JAHA.119.013958 10.1111/ijcp.13389 10.1007/s42979-020-00365-y 10.1161/hypertensionaha.120.15885 10.1109/access.2019.2923707 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Rajkumar Gangappa Nadakinamani et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Rajkumar Gangappa Nadakinamani et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Rajkumar Gangappa Nadakinamani et al. 2022 |
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| SubjectTerms | Algorithms Bayes Theorem Bayesian analysis Blood pressure Cardiovascular disease Cardiovascular diseases Cardiovascular Diseases - diagnosis Cholesterol COVID-19 Data Analysis Data mining Datasets Decision making Decision trees Health care industry Heart rate Humans Hypertension Learning algorithms Machine Learning Medical research Medicine, Experimental Methods Mortality Predictions Risk analysis Risk factors |
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| Title | Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques |
| URI | https://dx.doi.org/10.1155/2022/2973324 https://www.ncbi.nlm.nih.gov/pubmed/35069715 https://www.proquest.com/docview/2622087066 https://www.proquest.com/docview/2622486372 https://pubmed.ncbi.nlm.nih.gov/PMC8767405 https://downloads.hindawi.com/journals/cin/2022/2973324.pdf |
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