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.