Twin SVM-Based Classification of Alzheimer’s Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA

Table 12

Algorithm performance comparison over OASIS MRI data.

Algorithm	Accuracy	Sensitivity	Specificity	Precision
Proposed	96.68 ± 1.44	97.72 ± 2.34	95.61 ± 1.67	96.13 ± 1.57
DTCWT + PCA + TSVM	95.46 ± 1.35	97.55 ± 1.26	93.36 ± 2.39	94.15 ± 2.01
DWT + PCA + LDA + TSVM	87.23 ± 1.65	89.61 ± 2.25	84.85 ± 1.66	86.66 ± 1.99
DWT + PCA + TSVM	86.19 ± 1.50	88.83 ± 1.98	83.5 ± 1.87	85.66 ± 1.84
DTCWT + PCA + LDA + ANN	88.59 + 2.08	88.75 + 2.75	89.55 + 3.96	NA
DTCWT + PCA + LDA + KNN	83.69 + 1.57	85.7 + 1.94	81.8 + 1.45	NA
DTCWT + PCA + LDA + AdaBoost (tree)	87.45	88.59	86.26	NA
BRC + IG + SVM [26]	90.00 (77.41, 96.26)	96.88 (82.01, 99.84)	77.78 (51.92, 92.63)	NA
BRC + IG + Bayes [26]	92.00 (79.89, 97.41)	93.75 (77.78, 98.27)	88.89 (63.93, 98.05)	NA
BRC + IG + VFI [26]	78.00 (63.67, 88.01)	65.63 (46.78, 80.83)	100.00 (78.12, 100)	NA
MGM + PEC + SVM [30]	92.07 ± 1.12	86.67 ± 4.71	N/A	95.83 ± 5.89
GEODAN + BD + SVM [30]	92.09 ± 2.60	80.00 ± 4.00	NA	88.09 ± 5.33
TJM + WTT + SVM [30]	92.83 ± 0.91	86.33 ± 3.73	N/A	85.62 ± 0.85
VBM + RF [28]	89.0 ± 0.7	87.9 ± 1.2	90.0 ± 1.1	NA
DF + PCA + SVM [14]	88.27 ± 1.9	84.93 ± 1.21	89.21 ± 1.6	69.30 ± 1.91
EB + WTT + SVM + RBF [29]	86.71 ± 1.93	85.71 ± 1.91	86.99 ± 2.30	66.12 ± 4.16
EB + WTT + SVM + Pol [29]	92.36 ± 0.94	83.48 ± 3.27	94.90 ± 1.09	82.28 ± 2.78
Curvelet + PCA + KNN [27]	89.47	94.12	84.09	NA
US + SVDPCA + SVM-DT [25]	90	94	71	NA