| Work ref. | ML method | Learning approach | ML problem | Size of data set | Number of test subjects | Target group | Involve HD patients |
| [8] | ANN, MLP | Supervised | Classification | — | 21 | PD, healthy | No | [9] | RBFNN | Supervised | Regression | — | — | PD | No | [10] | DNN | Supervised | Classification | — | 12 | PD (8), healthy (4) | No | [11] | Decision tree, ID3 | Supervised | Classification | 195 | 31 | PD (23), healthy (8) | No | [15] | Adaptive neurofuzzy | Hybrid | 100 | — | PD | No | [16] | Neurofuzzy system | Hybrid | — | — | ALS | No | [17] | Fusion of classifiers (Bayesian, SVM, k-nearest neighbor) | Hybrid | 640 | ALS (13), PD (15), HD (16), healthy (16) | ALS, PD, HD | Yes | [18] | Neurofuzzy system | Hybrid | — | — | Only survey was done | No | [19] | ANN + MLP, RBFNN | Hybrid | — | — | PD | No | [20] | Neurofuzzy system | Hybrid | — | — | — | No | [21] | PBL-McRBFN | Supervised | Classification | 22,283 | 72 (50 PD, 22 healthy) | PD, healthy | No | [22] | Multistate Markov model | Hybrid | 2500 | 72 (82 PD, 62 healthy) | PD, healthy | No | [23] | Random tree, (C-RT), ID3, binary logistic regression, k-NN, (PLS), (SVM) | Supervised | Classification | 195 | 31 (23 PD, 8 healthy) | PD, healthy | No | [24] | FCM | Unsupervised | Clustering | 195 | — | PD | No |
|
|