Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches
| Autor: | Noryanti Muhammad, Mirza Rizwan Sajid, Mansour Alzahrani, Waqas Sami, Arshad Ali Khan, Christophe Chesneau, Bader A. Almehmadi, Ahmad Shahbaz, Asif Hanif |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Matching (statistics) nonlaboratory-based features Computer science Health Toxicology and Mutagenesis Machine learning computer.software_genre Risk prediction models Logistic regression Risk Assessment Article LMICs Machine Learning features importance Feature (machine learning) Humans machine learning models Aged risk prediction models Artificial neural network business.industry Public Health Environmental and Occupational Health Reproducibility of Results Middle Aged Support vector machine Cardiovascular Diseases Case-Control Studies Medicine Artificial intelligence Risk assessment business computer |
| Zdroj: | International Journal of Environmental Research and Public Health; Volume 18; Issue 23; Pages: 12586 International Journal of Environmental Research and Public Health International Journal of Environmental Research and Public Health, Vol 18, Iss 12586, p 12586 (2021) |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph182312586 |
| Popis: | Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low–middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case–control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities. |
| Databáze: | OpenAIRE |
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