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Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches

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  • Mirza Rizwan Sajid

    (Department of Statistics, University of Gujrat, Gujrat 50700, Pakistan)

  • Bader A. Almehmadi

    (Department of Internal Medicine, College of Medicine, Majmaah University, Almajmaah 11952, Saudi Arabia)

  • Waqas Sami

    (Department of Community Medicine and Public Health, College of Medicine, Majmaah University, Almajmaah 11952, Saudi Arabia
    Azra Naheed Medical College, Superior University, Lahore 54000, Pakistan)

  • Mansour K. Alzahrani

    (Department of Family Medicine, College of Medicine, Majmaah University, Almajmaah 11952, Saudi Arabia)

  • Noryanti Muhammad

    (Centre of Excellence for Data Science and Artificial Intelligence, Universiti Malaysia Pahang, Kuantan 26300, Malaysia
    Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Kuantan 26300, Malaysia)

  • Christophe Chesneau

    (Department of Mathematics, University of Caen-Normandie, 14032 Caen, France)

  • Asif Hanif

    (University Institute of Public health, Faculty of Allied Health Sciences, University of Lahore, Lahore 54000, Pakistan)

  • Arshad Ali Khan

    (Faculty of Computing, Universiti Malaysia Pahang, Pekan 26600, Malaysia)

  • Ahmad Shahbaz

    (Department of Cardiac Surgery, Punjab Institute of Cardiology, Lahore 54000, Pakistan)

Abstract

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.

Suggested Citation

  • Mirza Rizwan Sajid & Bader A. Almehmadi & Waqas Sami & Mansour K. Alzahrani & Noryanti Muhammad & Christophe Chesneau & Asif Hanif & Arshad Ali Khan & Ahmad Shahbaz, 2021. "Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches," IJERPH, MDPI, vol. 18(23), pages 1-16, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12586-:d:690906
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    References listed on IDEAS

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    1. Stephen F Weng & Jenna Reps & Joe Kai & Jonathan M Garibaldi & Nadeem Qureshi, 2017. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    2. Sabri Boughorbel & Fethi Jarray & Mohammed El-Anbari, 2017. "Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
    3. Shelda Sajeev & Stephanie Champion & Alline Beleigoli & Derek Chew & Richard L. Reed & Dianna J. Magliano & Jonathan E. Shaw & Roger L. Milne & Sarah Appleton & Tiffany K. Gill & Anthony Maeder, 2021. "Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning," IJERPH, MDPI, vol. 18(6), pages 1-14, March.
    4. Shinya Suzuki & Takeshi Yamashita & Tsuyoshi Sakama & Takuto Arita & Naoharu Yagi & Takayuki Otsuka & Hiroaki Semba & Hiroto Kano & Shunsuke Matsuno & Yuko Kato & Tokuhisa Uejima & Yuji Oikawa & Minor, 2019. "Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-14, September.
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