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Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach

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

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Nonclinical features, Cardiovascular diseases, Machine learning algorithms, Risk prediction models, Cost-effective model
Subjects: Q Science > QA Mathematics
R Medicine > R Medicine (General)
Faculty/Division: Institute of Postgraduate Studies
Center for Mathematical Science
Depositing User: Noorul Farina Arifin
Date Deposited: 26 Aug 2021 04:22
Last Modified: 26 Aug 2021 04:22
URI: http://umpir.ump.edu.my/id/eprint/31891
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