Table 2. Efficiency Evaluation of intelligent systems for MS diagnosis.
| Reasoning Methods | Algorithm/technique/model | Indicator Evaluation result | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| sensitivity | specificity | accuracy | AUC | Positive predictive value | Negative predictive value | Kappa | Precision | comments | ||
| Fuzzy logic | Sugeno model (40) | 0.87 | 0.7562 | -- | 0.85 | |||||
| FCM (41) | 1 | |||||||||
| Mamedani model (38) | increasing efficiency, | |||||||||
| Mamedani model (37) | accuracy is very good | |||||||||
| Mamedani model (39) | high performance | |||||||||
| Inductive Machine Learning (ML) Approach | Decision tree (55) | 0.93 | 0.97 | |||||||
| Genetic programming | Genetic algorithms (55) | 0.93 | 0.75 | 0.9 | ||||||
| natural language processing | Perl algorithm (43) | 0.94 | 0.81 | 0.9 | 0.88 | |||||
| Perl algorithm (44) | 0.94 | 0.91 | 0.93 | 0.82 | ||||||
| Definitive type 1, Definitive type 2, possible type 1, possible type 2 algorithms (42) | 0.95 | 0.89 | 0.94 | 0.89 | ||||||
| Artificial Neural Network | MLP (46) LVQ (46) RBF (46) |
0.96 0.91 0.99 |
||||||||
| MLP (47) | 0.97 | 0.82 | 0.92 | |||||||
| neural net clamping technique (49) | 0.92 | 0.63 | 0.84 | |||||||
| Support vector machine | ---(51) | BAR=0.85 | ||||||||
| ---(50) | 0. 77 | 0.66 | 0.71 | 0.7 | 0.74 | |||||
| Statistical analysis | MLR2 MLR5 (49) |
0.94 0.95 |
0.54 0.54 |
0.84M 0.86 |
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| Linear Discriminant Analysis (LDA) (51) | BAR=0.87 | |||||||||
| Systematic approach (54) | - | - | - | High sensitivity and specificity | ||||||
| Evidence-Based | --- (53) | 1 | ||||||||
| Rule-based | Ambulation-based EDSS algorithm (32) | 0.69 | ||||||||
| Backward chaining (19) | . | Diagnosis of system near possible as a human expert | ||||||||
| Backward chaining (21) | Accurate result | |||||||||
| Backward chaining (20) | 0.8 | |||||||||
| RETE Algorithm (22) | Accurate result | |||||||||
| Case-based | Case Retrieval Net (33) | Successful diagnosis | ||||||||
| Model-based | Matching algorithm and OLAP-tool (34) | 0.95 | ||||||||
| Linear mathematical model (35) | 1 | |||||||||
| Compound methods | ||||||||||
| Case-based and rule-based | Backward chaining and (1) Euclidean Distance (2) Manhattan Distance (3) Mahalanobis distance (24) |
0.93 | 0.866 | 0.87 0.82 0.84 |
Mean Error Rate=13.23 Mean Error Rate=17071Mean Error Rate=13.23 |
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| Backward chaining (23) | -- | -- | -- | -- | -- | High performance | ||||
| Support vector machine and Statistical analysis | ---- LR (52) |
0.86 | ||||||||
| Support vector machine and Artificial neural network | --- RFB (48) |
-- | -- | 0.91 | efficiency=0.69 | |||||
| Statistical analysis and Inductive machine learning approach and Artificial neural network | Naïve Bayes and Random decision and FFBP (45) | 0.93 | 0.86 | 0.8 | 0.9 | |||||
| Fuzzy logic and rule-based | Fuzzy cluster means (FCM) and Forward chaining (31) | highly accurate results | ||||||||