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. 2020 Jun 18;72(4):258–264. doi: 10.1016/j.ihj.2020.06.004

Table 2.

Machine learning-based CVD/Stroke risk stratification.


#SN
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
Authors AT (Modality) Features Types TF Classifier
Type
Ground
Truth
N∗ TI Training
Protocol
Performance
Evaluation
Benchmarking
R1 Kariacou et al17 (2012) Carotid (CUS) Image-based Texture 27 SVM, LR Follow-up data labels 108 ACC (77%)
R2 Acharya et al18 (2013) Carotid (CUS) Grayscale Features 17 SVM, GMM, RBPNN, DT, kNN, NBC, FC Labels from Physicians 445 492 K3 DB1:Accuracy (93.1%)
DB1:Accuracy (85.3%)
R3 Acharya et al72 (2014) Carotid (CUS) Phenotypes & HoS Features 7 SVM, RBPNN, kNN, DT Labels from physicians 59 118 K10 Accuracy (99.1%)
R4 Gastounioti et al19 (2015) Carotid (CUS) Kinematics Features 1236 SVM Follow-up data labels 56 4200 Accuracy (88%) Against kNN, PNN, DT, DA
R5 Araki et al20 (2017) Carotid (CUS) Image-based Texture Features 16 SVM LD-based risk labels 204 407 K5, K10,
JK
Accuracy (NW: 95.08% & FW: 93.47%)
R6 Saba et al21 (2017) Carotid (CUS) Image-based Texture 16 SVM LD-based risk labels 204 407 K10 Accuracy (NW: 98.83% & FW: 98.55%)
R7 Weng et al22 (2017) CRF 30 RF, LR, GBM, ANN Follow-up data labels 378256 K4 AUC: 0.764 Against PCRS
R8 Kakadiaris et al23 (2018) CRF 9 SVM Follow-up data labels 6459 K2 Se (86%),
Sp (95%),
AUC (0.92)
Against PCRS
R9 Proposed (2019) Carotid (CUS) Integrated Features 38 RF Labels from physicians 202 395 K2, K5, K10, JK AUC: 0.99 Against Conventional

CUS: Carotid ultrasound, LR: Logistic Regression, SVM: Support Vector Machine; Se: Sensitivity, Sp: Specificity; DWT: Discrete Wavelet Transform, kNN: K-Nearest Neighbor, RBPNN: Radial Basis Probabilistic Neural Network, GMM: Gaussian Mixture Model, NBC: Naïve Bays Classifier, FC: Fuzzy Classifier, DB: Database, HoS: Higher order Spectra, LBP: Local Binary Pattern, FDR: Fisher Discriminant Ratio, WRS: Wilcoxon Rank-Sum, PCA: Principal Component Analysis, DA: Discriminant Analysis, MLP: Multilayer Perceptron, RF: Random Forest, BS: Brier Score, QNN: Quantum Neural Network, IGR: Information Gain Ranking, MDMST: Minimal Depth of Maximal Subtree, SOM: Self Organization Map, FRS: Framingham Risk score, PCRD: Pooled Cohort Risk Score.