An ensemble ofk-nearest neighbours algorithm for detection of Parkinson's disease

Abstract

Parkinson's disease is a disease of the central nervous system that leads to severe difficulties in motor functions. Developing computational tools for recognition of Parkinson's disease at the early stages is very desirable for alleviating the symptoms. In this paper, we developed a discriminative model based on a selected feature subset and applied several classifier algorithms in the context of disease detection. All classifier performances from the point of both stand-alone and rotation-forest ensemble approach were evaluated on a Parkinson's disease data-set according to a blind testing protocol. The new method compared to hitherto methods outperforms the state-of-the-art in terms of both predictions of accuracy (98.46%) and area under receiver operating characteristic curve (0.99) scores applying rotation-forest ensemble k-nearest neighbour classifier algorithm.

An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default

“…Another common default driver is the short‐term (current) liabilities. This is consistent with the findings of Gök (). The business intuition is that current liabilities is a significant indicator of short‐term debt.…”

A Comparison of Classification Methods for Telediagnosis of Parkinson’s Disease

“…Polat [33] tried a new method named fuzzy c-means clustering-based feature weighting and Acevedo et al [34] tried an alpha-beta bidirectional associative memory approach and they reported the accuracies of their classifiers as 97.93% and 97.17%, respectively. Ozcift [32] applied the IBk (a k-Nearest Neighbor variant) method and attained 96.93% and Gök [35] used k-NN classifier and reached 98.46% accuracy. The accuracy of this study is the highest at 99.1% when 10-fold cross-validation was used with PCA.…”

“…Acevedo used an alpha-beta bidirectional associative memory (ABBAM) approach to differentiate between patients with PD and healthy people [34]. Gök selected features and used six different classifiers: Bayes net, linear SVM, radial basis SVM, k-NN, multilayer perceptron, and K-Star [35].…”

Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples

“…As for feature transformation, the frequently used algorithms are PCA (principle component analysis) [26, 27, 31, 49]. As for feature selection, the frequently used algorithms are NN (neural network) based [27–30, 32, 49], serial search based [2, 14, 29, 31], random based [32, 33, 48], p value based [2, 27–34], relevance based [35, 36] or entropy based [37], discrimination algorithm (DA) based [47]. As for classifier design, the predominantly used classifiers include a support vector machine (SVM) [1, 2, 14, 29, 32, 35, 38–41], KNN [1, 2, 26, 28, 40, 41, 47, 48, 49], random forest (RF) [2, 30, 36], Bayesian network [27, 28, 40, 42, 43, 48], discrimination algorithm (DA) [27, 29, 31, 37], probabilistic neural network (PNN) [27, 43] or decision tree [31, 40, 42, 44–46].…”

“…As for classifier design, the predominantly used classifiers include a support vector machine (SVM) [1, 2, 14, 29, 32, 35, 38–41], KNN [1, 2, 26, 28, 40, 41, 47, 48, 49], random forest (RF) [2, 30, 36], Bayesian network [27, 28, 40, 42, 43, 48], discrimination algorithm (DA) [27, 29, 31, 37], probabilistic neural network (PNN) [27, 43] or decision tree [31, 40, 42, 44–46]. Besides, several ensemble models were involved compared with single classifier [27, 47, 48, 50, 51]. …”