voice_papers.csv

Objectives|Type of diagnosis|"Source of
Data"|"Number of
subjects (n)"|"Machine learning
method(s) "|Outcomes|Year|Reference
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|Fuzzy neural system with 10-fold cross validation|1|2016|Abiyev and Abizade
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|RPART, C4.5, PART, Bagging CART, random forest, Boosted C5.0, SVM|0.9757|2019|Aich et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|DBN of 2 RBMs|0.94|2016|Al-Fatlawi et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|EFMM-OneR with 10- fold cross validation or 5-fold cross validation|0.9421|2019|Al-Sayaydeha and Mohammad
Classification of PD from HC|Diagnosis|UCI machine learning repository|40; 20 HC + 20 PD|linear regression, LDA, Gaussian naïve Bayes, decision tree, KNN, SVM-linear, SVM-RBF with leaveone-subject-out cross validation|0.7|2019|Ali et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|40; 20 HC + 20 PD|LDA-NN-GA with leave-one-subject-out cross validation|1|2019|Ali et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|NNge with AdaBoost with 10-fold cross validation |0.963|2018|Alqahtani et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|logistic regression, KNN, naïve Bayes, SVM, decision tree, random forest, DNN with 10-fold cross validation (deep NN)|0.95513|2018|Anand et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|MLP with a trainvalidation-test ratio of 50:20:30|0.9296|2012|Bakar et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|99; 28 HC + 71 PD|FKNN, SVM, KELM with 10-fold cross validation|0.9789|2018|Cai et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|40; 20 HC + 20 PD|SVM, logistic regression, ET, gradient boosting, random forest with train-test split ratio = 80:20|0.7603|2019|Celik and Omurca
Classification of PD from HC|Diagnosis|UCI machine learning repository|40; 20 HC + 20 PD|MLP, GRNN with a training-test ratio of 50:50|0.9042|2016|Cimen and Bolat
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|ECFA-SVM with 10- fold cross validation|0.9795|2017|Dash et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|40; 20 HC + 20 PD|fuzzy classifier with 10-fold cross validation, leave-oneout cross validation or a train-test ratio of 70:30 |1|2019|Dastjerd et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|averaged perceptron, BPM, boosted decision tree, decision forests, decision jungle, locally deep SVM, logistic regression, NN, SVM with 10-fold crossvalidation |0.912105|2017|Dinesh and He
Classification of PD from HC|Diagnosis|UCI machine learning repository|50; 8 HC + 42 PD |KNN, SVM, ELM with a train-validation ratio of 70:30|0.9643|2017|Erdogdu Sakar et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|252; 64 HC + 188 PD |CNN with leave-oneperson-out cross validation|0.869|2019|Gunduz
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|SVM, logistic regression, KNN, DNN with a train-test ratio of 70:30|0.98|2018|Haq et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|SVM-RBF, SVM-linear with 10-fold cross validation|0.99|2019|Haq et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|LS-SVM, PNN, GRNN with conventional (train-test ratio of 50:50) and 10-fold cross validation|1|2014|Hariharan et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|random tree, SVMlinear, FBANN with 10-fold cross validation|0.9737|2014|Islam et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|SVM-linear with 5-fold cross validation |0.87|2012|Ji and Li
Classification of PD from HC|Diagnosis|UCI machine learning repository|40; 20 HC + 20 PD|decision tree, random forest, SVM, GBM, XGBoost|0.725|2018|Junior et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD |CART, SVM, ANN|0.9384|2020|Karapinar Senturk
Classification of PD from HC|Diagnosis|UCI machine learning repository|71; 28 HC + 43 PD|EWNN with a train-test ratio of 90:10 and cross validation|0.9|2018|Khan et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|40; 20 HC + 20 PD|stacked generalization with CMTNN with 10-fold cross validation |0.7|2015|Kraipeerapun and Amornsamank ul
Classification of PD from HC|Diagnosis|UCI machine learning repository|40; 20 HC + 20 PD|HMM, SVM|0.9516|2019|Kuresan et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|IGWO-KELM with 10- fold cross validation|0.9745|2017|Li et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|SCFW-KELM with 10- fold cross validation|0.9949|2014|Ma et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|SVM-RBF with 10-fold cross validation|0.9629|2016|Ma et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|logistic regression, NN, SVM, SMO, Pegasos, AdaBoost, ensemble selection, FURIA, rotation forest Bayesian network with 10-fold crossvalidation|0.9706|2013|Mandal and Sairam
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|logistic regression, KNN, SVM, naïve Bayes, decision tree, random forest, ANN|0.9487|2018|Marar et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|rotation forest ensemble with 10-fold cross validation|0.871|2011|Ozcift and Gulten
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|rotation forest ensemble|0.9693|2012|Ozcift
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|SVM-RBF with 10-fold cross validation or a train-test ratio of 50:50|0.9895|2016|Peker
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|ELM with 10-fold cross validation|0.8872|2016|Shahsavari et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|ensemble learning with 10‐fold cross validation|0.906|2019|Sheibani et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|decision tree classifier, logistic regression, SVM with 10-fold cross validation|0.76|2011|Yadav et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|80; 40 HC + 40 PD|KNN, SVM with 10- fold cross validation|0.9125|2019|Yaman et al
Classification of PD from HC|Diagnosis|UCI machine learning repository|31; 8 HC + 23 PD|MAP, SVM-RBF, FLDA with 5-fold cross validation|0.918|2014|Yang et al
Classification of PD from HC and assess the severity of PD |Diagnosis|collected from participants|52; 9 HC + 43 PD|SVM-RBF with cross validation |0.818|2014|Frid et al
Classification of PD from HC|Diagnosis|collected from participants|54; 27 HC + 27 PD|SVM with stratified 10- fold cross validation or leave-one-out cross validation|0.944|2018|Montaña et al
Classification of PD from HC|Diagnosis|collected from participants|40; 20 HC + 20 PD|KNN, SVM-linear, SVM-RBF with leaveone-subject-out or summarized leaveone-out|0.775|2013|Sakar et al
Classification of PD from HC|Diagnosis|collected from participants|78; 27 HC + 51 PD|KNN, SVM-linear, SVM-RBF, ANN, DNN with leave-one-out cross validation |0.8462|2017|Sztahó et al
Classification of PD from HC and assess the severity of PD |Diagnosis|collected from participants|88; 33 HC + 55 PD|KNN, SVM-linear, SVM-RBF, ANN, DNN with leave-onesubject-out cross validation|0.893|2019|Sztahó et al
Classification of PD from HC|Diagnosis|collected from participants|43; 10 HC + 33 PD|random forests, SVM with 10-fold cross validation and a traintest ratio of 90:10|0.986|2012|Tsanas et al
Classification of PD from HC|Diagnosis|PC-GITA database|100; 50 HC + 50 PD|ResNet with trainvalidation ratio of 90:10|0.917|2019|Wodzinski et al