SVM Multi-classification of T2D/CVD Patients Using Biomarker Features

Buddi, Sai; Taylor, Thomas J.; Borges, Chad R.; Nelson, Randall W.

doi:10.1109/icmla.2011.182

Cited by 2 publications

(12 citation statements)

References 16 publications

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“…The statistical distributions derived from the selected studies are shown in Figure 2 The percentage of studies for each of the three kinds of CVD risk prediction had the following distributions: multiclass (26%) [69][70][71][72][73][74][75][76][77][78][79][80][81][82], multi-label (15%) [83][84][85][86][87][88][89][90], and ensemble (59%) [80, (Figure 2a). Several different kinds of risk classes were identified in multiclass CVD framework, namely binary (65%), tertiary (22%), quaternary (6%), and greater than quaternary (7%) (Figure 2b).…”

Section: Statistical Distributionmentioning

confidence: 99%

“…The distribution of the ML-based CVD studies with and without feature selection are shown in Figure 2c. It was found that almost 82% of MLbased CVD studies performed feature selection for risk prediction whereas only 18% [69,70,73,75,83,94,96,110,120] did not perform it. For the ML-based multi-label CVD (Figure 2d), the total number of GT's used for each study were as follows and given in the ground braces: Venkatesh et al The percentage of studies for each of the three kinds of CVD risk prediction had the following distributions: multiclass (26%) [69][70][71][72][73][74][75][76][77][78][79][80][81][82], multi-label (15%) [83][84][85][86][87][88][89][90], and ensemble (59%) [80, (Figure 2a).…”

Section: Statistical Distributionmentioning

confidence: 99%

“…It was found that almost 82% of MLbased CVD studies performed feature selection for risk prediction whereas only 18% [69,70,73,75,83,94,96,110,120] did not perform it. For the ML-based multi-label CVD (Figure 2d), the total number of GT's used for each study were as follows and given in the ground braces: Venkatesh et al The percentage of studies for each of the three kinds of CVD risk prediction had the following distributions: multiclass (26%) [69][70][71][72][73][74][75][76][77][78][79][80][81][82], multi-label (15%) [83][84][85][86][87][88][89][90], and ensemble (59%) [80, (Figure 2a). Several different kinds of risk classes were identified in multiclass CVD framework, namely binary (65%), tertiary (22%), quaternary (6%), and greater than quaternary (7%) (Figure 2b).…”

Section: Statistical Distributionmentioning

confidence: 99%

“…The distribution of the ML-based CVD studies with and without feature selection are shown in Figure 2c. It was found that almost 82% of ML-based CVD studies performed feature selection for risk prediction whereas only 18% [69,70,73,75,83,94,96,110,120] did not perform it. For the ML-based multi-label CVD (Figure 2d), the total number of GT's used for each study were as follows and given in the ground braces: Venkatesh et al 4) [90].…”

Section: Statistical Distributionmentioning

confidence: 99%

“…The types of covariates considered for the multiclass systems were OBBM [71,76,80,82], LBBM [71,76,80,82], CUSIP [76,80] for office-based setups (Table 1: row 1-5), and Electrocardiogram (ECG) [79,81,82], PCG [77], Acceleration Plethysmogram (APG) [78] signals for cardiac stress test laboratories (Table 1: row 6-9), and coronary artery calcium (CAC) for CTbased CVD models [135]. The ground truths considered for CVD risk assessment (Table 1) were death [80], coronary heart disease (CHD) [82], chronic heart conditions (CHC) [79], cardiovascular event (CVE) [76], sudden cardiac death (SCD) [72], heart failure (HF), myocardial infarction (MI) [75], coronary artery calcification (CAC) score [69], fatal/non-fatal CVD [73], joint CVD and diabetes [70]. Note that these gold standard choices along with AI attributes, scientific and clinical validations are key to preventing bias in AI.…”

Section: Cvd-based Multiclass Risk Assessment Systemmentioning

confidence: 99%

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A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Suri¹,

Bhagawati

Sauseng

et al. 2022

Diagnostics

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Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

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