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Table 6 The efficacy of ANN, logistic regression, kNN and SVM in testing sets

From: Application of artificial neural network model in diagnosis of Alzheimer’s disease

Assessment index ANN (95% CI) Logistic regression (95% CI) kNN (95% CI) SVM (95% CI)
Sensitivity (%) 87.28 (85.12–88.34) 71.36 (69.18–72.93) 75.42 (73.24–76.17) 84.23(82.78–85.63)
Specificity (%) 94.74 (92.61–95.75) 90.25 (87.82–91.85) 89.39 (87.65–90.49) 90.45(88.65–91.73)
Accuracy (%) 92.13 (89.48–92.57) 83.92 (80.31–85.11) 84.91 (83.01–86.41) 86.04(84.93–88.10)
PPV (%) 88.64 (86.83–90.06) 73.11 (71.09–74.86) 83.02 (82.17–85.23) 82.36 (81.25–83.94)
NPV (%) 91.28 (90.41–93.10) 86.37 (85.19–88.26) 86.41 (84.85–88.15) 88.77(87.21–90.33)
AUC 0.897 (0.877–0.915) 0.804 (0.781–0.818) 0.832 (0.817–0.849) 0.864 (0.853–0.876)