Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach
Background In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/31891/ http://umpir.ump.edu.my/id/eprint/31891/1/Nonclinical%20Features%20in%C2%A0Predictive%20Modeling.pdf |
| _version_ | 1848823883429314560 |
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| author | Mirza Rizwan, Sajid Noryanti, Muhammad Roslinazairimah, Zakaria Ahmad, Shahbaz Syed Ahmad Chan, Bukhari Kadry, Seifedine A., Suresh |
| author_facet | Mirza Rizwan, Sajid Noryanti, Muhammad Roslinazairimah, Zakaria Ahmad, Shahbaz Syed Ahmad Chan, Bukhari Kadry, Seifedine A., Suresh |
| author_sort | Mirza Rizwan, Sajid |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Background
In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms. |
| first_indexed | 2025-11-15T03:04:13Z |
| format | Article |
| id | ump-31891 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:04:13Z |
| publishDate | 2021 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-318912021-08-26T04:22:58Z http://umpir.ump.edu.my/id/eprint/31891/ Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach Mirza Rizwan, Sajid Noryanti, Muhammad Roslinazairimah, Zakaria Ahmad, Shahbaz Syed Ahmad Chan, Bukhari Kadry, Seifedine A., Suresh QA Mathematics R Medicine (General) Background In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms. Springer 2021 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31891/1/Nonclinical%20Features%20in%C2%A0Predictive%20Modeling.pdf Mirza Rizwan, Sajid and Noryanti, Muhammad and Roslinazairimah, Zakaria and Ahmad, Shahbaz and Syed Ahmad Chan, Bukhari and Kadry, Seifedine and A., Suresh (2021) Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach. Interdisciplinary Sciences: Computational Life Sciences, 13. pp. 201-211. ISSN 1913-2751. (Published) https://doi.org/10.1007/s12539-021-00423-w https://doi.org/10.1007/s12539-021-00423-w |
| spellingShingle | QA Mathematics R Medicine (General) Mirza Rizwan, Sajid Noryanti, Muhammad Roslinazairimah, Zakaria Ahmad, Shahbaz Syed Ahmad Chan, Bukhari Kadry, Seifedine A., Suresh Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach |
| title | Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach |
| title_full | Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach |
| title_fullStr | Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach |
| title_full_unstemmed | Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach |
| title_short | Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach |
| title_sort | nonclinical features in predictive modeling of cardiovascular diseases: a machine learning approach |
| topic | QA Mathematics R Medicine (General) |
| url | http://umpir.ump.edu.my/id/eprint/31891/ http://umpir.ump.edu.my/id/eprint/31891/ http://umpir.ump.edu.my/id/eprint/31891/ http://umpir.ump.edu.my/id/eprint/31891/1/Nonclinical%20Features%20in%C2%A0Predictive%20Modeling.pdf |