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Machine learning framework for atherosclerotic cardiovascular disease risk assessment

  • Research article
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Journal of Diabetes & Metabolic Disorders Aims and scope Submit manuscript

Abstract

Introduction

Atherosclerotic cardiovascular disease (ASCVD) is the first leading cause of mortality globally. To identify the individual risk factors of ASCVD utilizing the machine learning (ML) approaches.

Materials & methods

This cohort-based cross-sectional study was conducted on data of 500 participants with ASCVD among Tabriz University Medical Sciences employees, during 2020. The data with ML methods were developed and validated to predict ASCVD risk with naive Bayes (NB), spurt vesture machines (SVM), regression tree (RT), k-nearest neighbors (KNN), artificial neural networks (ANN), generalized additive models (GAM), and logistic regression (LR).

Results

Accuracy of the models ranged from 95.7 to 98.1%, with a sensitivity of 50.0 to 97.3%, specificity of 74.3 to 99.1%, positive predictive value (PPV) of 0.0 to 98.0%, negative predictive value (NPV) of 68.4 to 100.0%, positive likelihood ratio (LR +) of 13.8 to 96.4%, negative likelihood ratio (LR-) of 3.6 to 51.9%, and area under ROC curve (AUC) of 62.5 to 99.4%. The ANN fit the data best with an accuracy of 98.1% (95% CI: 96.5–99.1), a specificity of 99.1% (95% CI: 97.7–99.9), a LR + of 96.4% (95% CI: 36.2–258.8), and AUC of 99.4% (95% CI: 85.2–97.0). Based on the optimal model, sex (females), age, smoking, and metabolic syndrome were shown to be the most important risk factors of ASCVD.

Conclusion

Sex (females), age, smoking, and metabolic syndrome were predictors obtained by ANN. Considering the ANN as the optimal model identified, more accurate prevention planning may be designed.

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Data availability

The data that support the findings of this study are available from MAJ, but restrictions are applied to the availability of these data, which were used under license for the current study, and are not publicly available. Data are, however, available from the authors upon reasonable request by MAJ.

Code availability

All precision used in this study was carried out by STATISTICA software which is menu based the for the is no code available.

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Acknowledgements

We would like to appreciate the collaboration of the department of biostatistics and epidemiology, Faculty of Health, and Tabriz University of medical sciences, for providing the environment for modeling data and manuscript writing.

Funding

This study was supported by a research deputy of Tabriz University of medical sciences.

Author information

Authors and Affiliations

  1. Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Parya Esmaeili

  2. Department of Epidemiology and Biostatistics, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran

    Parya Esmaeili, Saeid Mousavi & Mohammad Asghari-Jafarabadi

  3. Cardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Neda Roshanravan & Samad Ghaffari

  4. Endocrine Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Naimeh Mesri Alamdari

  5. Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

    Mohammad Asghari-Jafarabadi

  6. Cabrini Research, Cabrini Health, 154 Wattletree Rd, Malvern, VIC, 3144, Australia

    Mohammad Asghari-Jafarabadi

  7. School of Public Health and Preventative Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC, 3800, Australia

    Mohammad Asghari-Jafarabadi

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  1. Parya Esmaeili
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Contributions

All authors read and approved the final manuscript. MAJ and PE conceived the study and participated in the design and data collection. MAJ, PE, NR, SM, SG, and NMA participated in the data analysis and manuscript preparation.

Corresponding author

Correspondence to Mohammad Asghari-Jafarabadi.

Ethics declarations

Ethics approval

The institutional review board of Tabriz University of medical sciences approved the protocol of the study (ethics code: IR.TBZMED.REC.1400.1006).

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The participants' privacy was preserved. All participants filled and signed the informed consent and assent. All methods were carried out in accordance with relevant guidelines and regulations.

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Not applicable.

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The authors have no relevant financial or non-financial interests to disclose.

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Esmaeili, P., Roshanravan, N., Mousavi, S. et al. Machine learning framework for atherosclerotic cardiovascular disease risk assessment. J Diabetes Metab Disord 22, 423–430 (2023). https://doi.org/10.1007/s40200-022-01160-7

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  • DOI: https://doi.org/10.1007/s40200-022-01160-7

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