Table 3 Explains different methods used by the researchers for the diagnosis of AD and various parameters such as accuracy, sensitivity, specificity, and precision achieved with the different models of machine learning.
References | Method | AD:NC Acc | SEN recall | SPE | Precision | AUC |
|---|---|---|---|---|---|---|
Zhang D et al. [3] | Multi-kernel SVM | 78.40 | 79.00 | 78.00 | – | 76.80 |
Suk H et al. [4] | Group sparse representation + SVM | 89.19 | 91.00 | 88.00 | – | 95.60 |
Lu D et al. [6] | DNN | 82.93 | 79.69 | 83.84 | – | – |
Tong T et al. [7] | Multiple instance learning + SVM | 89.00 | – | – | – | – |
Lee G et al. [9] | Multi- model deep learning + RNN | 81.00 | 84.00 | 80.00 | – | 86.00 |
Xiao Z et al. [10] | SVM-REF with covariance | 85.71 | 79.63 | 91.38 | – | – |
Ortiz A et al. [12] | Deep belief network (SVM) | 90.00 | 86.00 | 94.00 | – | 95.00 |
Subramoniam M et al.(a) [14] | Vanilla DNN | 95.31 | – | – | – | – |
Subramoniam M et al.(b) [14] | CNN | 95.32 | – | – | – | – |
Yue L et al.(a) [23] | CNN | 99.40 | 100 | – | 98.80 | 97.20 |
Yue L et al.(b) [23] | CNN | 98.60 | 97.20 | – | 100 | 98.60 |
Goryawala M et al. [24] | Linear regression model + LDA | 93.90 | 96.30 | 89.50 | 93.80 | – |
Aderghal [25] | 2D CNN | 91.41 | 93.75 | 89.60 | – | – |
Korolev (a) [26] | 3D CNN | 79.00 | – | – | – | 88.00 |
Korolev (b) [26] | ResNet | 80.00 | – | – | – | 87.00 |
Cheng and Liu [27] | RNN | 91.20 | 91.40 | 83.84 | 95.30 | – |
Choi H et al. [28] | 3D CNN | 96.00 | – | – | – | 91.00 |
Cheng and Liu [29] | 3D CNN | 89.60 | 87.10 | 92.00 | – | 94.45 |
Kumar N et al. [30] | Linear discriminant analysis | 98.92 | – | – | – | – |
Ji h et al. [31] | CNN | 98.59 | 97.22 | 100 | – | – |
Battineni G et al. [32] | Linear regression model | 98.30 | 97.40 | – | 98.60 | 99.70 |
Kumari R et al. [33] | CNN | 90.25 | 85.53 | – | – | – |
Alickovic E et al. [34] | Random forest | 85.77 | 54.17 | 97.44 | – | – |
Madiwalar S et al. [35] | Extra tree | 93.14 | 85.00 | – | 85.00 | – |
Pan D et al. [36] | CNN | 84.00 | – | – | – | 92.00 |
Alroobaea R et al. (a) [37] | Random forest | 98.89 | 99.19 | – | 98.89 | – |
Alroobaea R et al. (b) [37] | Logistic regression | 84.33 | 84.14 | – | 84.54 | – |
Savita et al. [38] | SVM | 85.80 | – | – | 87.83 | – |
Li and Yang [39] | SVM | 90.00 | 93.90 | 85.10 | – | 97.00 |
Mehmood A et al. [40] | CNN | 98.73 | 98.19 | 99.09 | – | – |