Table 3.
Machine learning-based CVD/stroke risk stratification in non-RA cohorts
| C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 |
|---|---|---|---|---|---|---|---|---|---|
| SN | First Author (Year) | N | Features types | TF | Feature Selection | Classifier type | Gold standard | PE | Benchmarking |
| R1 | Gastounioti (2015) [116] | 56 | Kinematics features | 1236 | FDR, WRS, PCA | SVM | Follow-up data labels | ACC (88%) | Against kNN, PNN, DT, DA |
| R2 | Unnikrishnan (2016) [117] | 2406 | CCVRFs | 9 | NA | SVM | Follow-up data labels | Se (68.2%), Sp (85.9%), AUC (0.71) | Against FRS |
| R3 | Venkatesh (2017) [118] | 6814 | CCVRFs, image phenotypes, and serum biomarkers | 735 | MDMST | RF, Cox, LASSO-cox, AIC-Cox backward regression | Follow-up data labels | C-Index (0.81), BS (0.083) | Against FRS and PCRS |
| R4 | Banchhor (2017) [119] | 22 | Texture-based and wall-based features | 65 | PCA | SVM | Carotid plaque burden | ACC (91.28%) AUC (0.91) | – |
| R5 | Araki (2017) [47] | 204 | Image-based texture features | 16 | Statistical Test | SVM | LD-based risk labels | ACC (NW: 95.08% & FW: 93.47%) | – |
| R6 | Weng (2017) [56] | 378,256 | CCVRFs | 30 | – | RF, LR, GBM, ANN | Follow-up data labels | AUC: 0.764 | Against PCRS |
| R7 | Kakadiaris (2018) [55] | 6459 | CCVRFs | 9 | – | SVM | Follow-up data labels | Se (86%), Sp (95%), AUC (0.92) | Against PCRS |
| R8 | Jamthikar (2019) [54] | 202 | CCVRFs and CUS Image-based features | 47 | PCA polling | RF | Carotid stenosis surrogate endpoint of CVD |
AUC of ML system = 0.80 (95% CI 0.77–0.84) AUC for CCVRC = 0.68 (95% CI 0.64–0.72) |
– |
| R9 | Jamthikar (2020) [51] | 202 | CCVRFs and CUS image-based features | 19 | – | SVM | Surrogate endpoint of CVD | AUC of ML system = 0.88 (p < 0.001) | Against 13 CCVRC |
| R10 | Jamthikar (2020) [120] | 202 | CCVRFs and CUS image-based features | 38 | Logistic regression | RF | LD as surrogate endpoint of CVD | AUC for integrated ML system = 0.99, p < 0.001 | – |
SN serial num, N Number of patients, CVD cardiovascular disease, CUS carotid ultrasound, LD lumen diameter, LR logistic regression, FDR fisher discriminant ratio, WRS Wilcoxon Rank-Sum, PCA principal component analysis, DA discriminant analysis, MDMST minimal depth of maximal subtree, SVM support vector machine, GMM Gaussian Mixture Model, RBPNN Radial Basis Probabilistic Neural Network, DT decision tree, kNN K-nearest neighbor, NB Naïve Bays, FC Fuzzy Classifier, QNN Quantum Neural Network, MLP Multilayer Perceptron, RF Random Forest, SOM Self Organization Map, ANN artificial neural network, DWT Discrete Wavelet Transform, HoS higher-order spectra, CCVRFs conventional cardiovascular risk factors, ACC accuracy, Se sensitivity, Sp specificity, AUC area under the curve, BS Brier Score, IGR information gain ranking, DB database, CCVRC conventional cardiovascular risk calculators, PCRS pooled cohort risk score, FRS Framingham risk score