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.
Highlights
The surge in cardiovascular diseases (CVDs) and cardiovascular mortality (CVM) has become a real challenge for healthcare systems [1]
We found that machine learning (ML)-based risk prediction models (RPMs) are capable of optimizing the predictive strengthofofmodels modelsthrough through the the exploration exploration of terms strength of unobserved unobservedpatterns patternsand andinteraction interaction terms in the data sets
This study found that ML models from the class of Artificial neural networks (ANN) and support vector machines (SVM) outperformed logistic regression analysis (LRA) in performance matrices
Summary
The surge in cardiovascular diseases (CVDs) and cardiovascular mortality (CVM) has become a real challenge for healthcare systems [1]. Preventive health policies in high-income countries (HICs) have created a substantial decline in CVDs and CVM in the last two decades [2,3]. This reduction in cause-specific morbidity and mortality reflects the success of preventive health policies, especially the usage of risk prediction models (RPMs) [4]. Existing RPMs have been developed, validated and implemented in HICs, the World Health Organization (WHO) states that almost 80% of CVMs occur in low–middle-income countries (LMICs) [5]. This situation is threatening to LMICs and literature suggests the development of locally customized but methodologically efficient RPMs for CVDs by considering the limitations of existing RPMs.
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