Journal of Healthcare Engineering / 2017 / Article / Tab 12

Research Article

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.

AlgorithmAccuracySensitivitySpecificityPrecision

Proposed96.68 ± 1.4497.72 ± 2.3495.61 ± 1.6796.13 ± 1.57
DTCWT + PCA + TSVM95.46 ± 1.3597.55 ± 1.2693.36 ± 2.3994.15 ± 2.01
DWT + PCA + LDA + TSVM87.23 ± 1.6589.61 ± 2.2584.85 ± 1.6686.66 ± 1.99
DWT + PCA + TSVM86.19 ± 1.5088.83 ± 1.9883.5 ± 1.8785.66 ± 1.84
DTCWT + PCA + LDA + ANN88.59 + 2.0888.75 + 2.7589.55 + 3.96NA
DTCWT + PCA + LDA + KNN83.69 + 1.5785.7 + 1.9481.8 + 1.45NA
DTCWT + PCA + LDA + AdaBoost (tree)87.4588.5986.26NA
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 ± 1.1286.67 ± 4.71N/A95.83 ± 5.89
GEODAN + BD + SVM [30]92.09 ± 2.6080.00 ± 4.00NA88.09 ± 5.33
TJM + WTT + SVM [30]92.83 ± 0.9186.33 ± 3.73N/A85.62 ± 0.85
VBM + RF [28]89.0 ± 0.787.9 ± 1.290.0 ± 1.1NA
DF + PCA + SVM [14]88.27 ± 1.984.93 ± 1.2189.21 ± 1.669.30 ± 1.91
EB + WTT + SVM + RBF [29]86.71 ± 1.9385.71 ± 1.9186.99 ± 2.3066.12 ± 4.16
EB + WTT + SVM + Pol [29]92.36 ± 0.9483.48 ± 3.2794.90 ± 1.0982.28 ± 2.78
Curvelet + PCA + KNN [27]89.4794.1284.09NA
US + SVDPCA + SVM-DT [25]909471NA

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