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Table 4 Accuracies of 7 classifiers from fivefold cross validation

From: Classification of Parkinson’s disease with freezing of gait based on 360° turning analysis using 36 kinematic features

 

ML techniques

PDs vs. Cons (with 36 features)

PDs vs. Cons (with 5 features)

F vs. NF (with 36 features)

F vs. NF (with 6 features)

Accuracy

LR

97.4 ± 2.8

98.0 ± 3.0

72.8 ± 0.8

72.9 ± 10.8

KNN

94.7 ± 6.9

96.7 ± 4.1

62.9 ± 8.7

61.9 ± 7.8

NB

91.6 ± 2.9*

96.7 ± 3.3*

70.6 ± 6.5

64.0 ± 9.0

LDA

96.1 ± 4.3

97.4 ± 3.6

71.8 ± 9.3

69.6 ± 12.3

QDA

98.0 ± 3.0

97.4 ± 2.8

72.6 ± 11.5

67.5 ± 10.9

SVM

98.0 ± 3.0

98.0 ± 3.0

69.4 ± 8.9

63.4 ± 16.8

RF

98.1 ± 1.8

98.0 ± 3.0

79.4 ± 6.9

70.8 ± 10.6

  1. Mean (%) ± standard deviations (%) were calculated through fivefold cross validation; mean values presented in boldface denote the best performance (the highest test accuracy)
  2. ML machine learning, PDs people with PD, Cons controls, F freezers, NF non-freezers, LR logistic regression, KNN k-nearest neighbors, NB Naïve Bayes, LDA linear discriminant analysis, QDA quadratic discriminant analysis, SVM support vector machine, RF random forest
  3. *Denotes a significant difference