| Knowledge Based Methods |
| 1. I. Galea (53) |
2015 |
Prediction and diagnosis |
Evidence-Based |
—- |
| 2. Ahmad A. Al-Hajji (20) |
2012 |
Diagnosis |
Rule- based |
Backward chaining |
| 3.Ayangbekun (21) |
2015 |
Diagnosis and treatment |
Rule- based |
Backward chaining |
| 4.RajdeepBorgohain (22) |
2016 |
Diagnosis |
Rule- base |
RETE algorithm |
| 5.AtulKrishan Sharma (19) |
2014 |
Diagnosis |
Rule-based |
Backward chaining |
| 6.YC Cohen (32) |
2000 |
Diagnosis and assessment of disability |
Rule–based |
Ambulation-based EDSS algorithm |
| 7.V.Kurbalija (33) |
2007 |
Diagnosis |
Case-based |
Case Retrieval Net |
| 8.YelizKaraca (35) |
2014 |
Diagnosis and prognosis of course disease |
Model- based |
Linear mathematical model |
| 9.M Daumer (34) |
2007 |
Diagnosis and prognosis of course disease |
Model- based |
Matching Algorithm and OLAP-tool |
| Non-knowledge based methods |
| 10.Mary F Davis (43) |
2013 |
Diagnosis and prognosis of course disease |
Natural language processing |
Perl algorithm |
| 11.Richard E. Nelson1(44) |
2016 |
Diagnosis and prognosis of course disease |
Natural language processing |
Perl algorithm |
| 12.Herbert S. Chase (42) |
2017 |
Diagnosis and prognosis of course disease |
Natural language processing |
Definitive type 1, Definitive type 2,possible type 1, possible type 2algorithms |
| 13.V. Wottschel (30) |
2015 |
Prediction and diagnosis |
Support vector machine (SVM) |
—- |
| 14.JM Nielsen (54) |
2007 |
Diagnosis |
Statistical analysis |
Systematic approach |
| 15.Adrian Ion M_argineanu (51) |
2017 |
Diagnosis |
(1) Statistical analysis2) Support Vector Machines (SVM) |
(1)Linear Discriminant Analysis (LDA) |
| 16.R. Linder (49) |
2009 |
Diagnosis |
(1)Artificial neural network (2) Statistical analysis |
(1) Neural net clamping technique (2) Multiple logistic regression(MLR2 , MLR5) |
| 17.Yeliz Karaca (46) |
2015 |
Diagnosis and prognosis of course disease |
Artificial neural network |
(1)Radial Basis Function (RBF)(2) Learning Vector Quantization (LVQ)3) Feed Forward Back Propagation(FFBP) |
| 18.YasharSarbaz (47) |
2017 |
Diagnosis |
Artificial neural network |
multilayer perceptron (MLP) with Feed Forward Back Propagation(FFBP) |
| 19.Imianvan Anthony (41) |
2012 |
Diagnosis |
Fuzzy logic |
Fuzzy cluster means(FCM) |
| 20.Ayangbekun (37) |
2015 |
Diagnosis |
Fuzzy logic |
Mamedani inference model |
| 21..Ali Amooji (38) |
2015 |
Diagnosis |
Fuzzy logic |
Mamedani inference model |
| 22.M.ArabzadehGhahazi (39) |
2014 |
Diagnosis |
Fuzzy logic |
Mamedani inference model |
| 23.Massimo Esposito (40) |
2011 |
Diagnosis |
Fuzzy logic |
Sugeno inference model |
| 24.G. Panagi (55) |
2012 |
Diagnosis |
(1)Genetic programming(2)Inductive machine learning approach |
(1)Genetic algorithm(2)Decision tree |
| Compound methods |
| 25.Bikram L.Shrestha (23) |
2008 |
Diagnosis |
Case-based and Rule-based |
Backward chaining |
| 26. Shiny Mathew (24) |
2015 |
Diagnosis |
Case-based and Rule-based |
Backward chaining and(1)Euclidean Distance2)Manhattan Distance(3)Mahalanobis distance |
| 27.Yijun Zhao (52) |
2017 |
Diagnosis |
Support vector machines (SVM)and Statistical analysis |
Logistic regression (LR) |
| 28.Gabriel Kocevar (48) |
2016 |
Diagnosis and prognosis of course disease |
Artificial neural network andSupport vector machine (SVM) |
Radial Basis Function(RBF) |
| 29.Bartolome Bejarano (45) |
2011 |
Prediction and diagnosis |
Statistical analysis andInductive machine learning approach and Artificial Neural Network |
Naïve Bayes AndRandom decision-tree meta-classifier and multilayer perceptron (MLP) with Feed Forward Back Propagation(FFBP) |
| 30.Fanis G. Kalatzis (31) |
2009 |
Diagnosis and prognosis |
Fuzzy logic AndRule-based |
Fuzzy cluster means(FCM) AndForward chaining |
