Artificial Intelligence (AI), in particular, machine learning (ML) has shown promising results in coronary artery disease (CAD) or cardiovascular disease (CVD) risk prediction. Bias in ML systems is of great interest due to its over-performance and poor clinical delivery. The main objective is to understand the nature of risk-of-bias (RoB) in ML and non-ML studies for CVD risk prediction.

Methods

PRISMA model was used to shortlisting 117 studies, which were analyzed to understand the RoB in ML and non-ML using 46 and 32 attributes, respectively. The mean score for each study was computed and then ranked into three ML and non-ML bias categories, namely low-bias (LB), moderate-bias (MB), and high-bias (HB), derived using two cutoffs. Further, bias computation was validated using the analytical slope method.

Results

Five types of the gold standard were identified in the ML design for CAD/CVD risk prediction. The low-moderate and moderate-high bias cutoffs for 24 ML studies (5, 10, and 9 studies for each LB, MB, and HB) and 14 non-ML (3, 4, and 7 studies for each LB, MB, and HB) were in the range of 1.5 to 1.95. Bias _ML < Bias _non-ML by ∼43%. A set of recommendations were proposed for lowering RoB.

Conclusion

ML showed a lower bias compared to non-ML. For a robust ML-based CAD/CVD prediction design, it is vital to have (i) stronger outcomes like death or CAC score or coronary artery stenosis; (ii) ensuring scientific/clinical validation; (iii) adaptation of multiethnic groups while practicing unseen AI; (iv) amalgamation of conventional, laboratory, image-based and medication-based biomarkers.

Highlights

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Risk-of-Bias (RoB) in Machine Learning (ML) studies for cardiovascular disease (CVD) risk prediction using 46 AI attributes.

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Mean score computed, ranked, plotted using slope method, and two cutoffs established, and evaluating the three categories of risk measured: low-bias (LB), moderate-bias (MB), and high-bias (HB)

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Validating the RoB computation using analytical slope method along with establishing the recommendations to lower the RoB.

References

[1]

S. Kaptoge, L. Pennells, D. De Bacquer, M.T. Cooney, M. Kavousi, G. Stevens, L.M. Riley, S. Savin, T. Khan, S. Altay, World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions, Lancet Global Health 7 (10) (2019) e1332–e1345.

Abstract

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