An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳s disease

Abstract

Abstract:In this paper, we explore the potential of extreme learning machine (ELM) and kernel ELM (KELM) for early diagnosis of Parkinson's disease (PD). In the proposed method, the key parameters including the number of hidden neuron and type of activation function in ELM, and the constant parameter C and kernel parameter γ in KELM are investigated in detail. With the obtained optimal parameters, ELM and KELM manage to train the optimal predictive models for PD diagnosis. In order to further improve the performance of ELM and KELM models, feature selection techniques are implemented prior to the construction of the classification models. The effectiveness of the proposed method has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the ROC (receiver operating characteristic) curve (AUC). Compared to the existing methods in previous studies, the proposed method has achieved very promising classification accuracy via 10-fold cross-validation (CV) analysis, with the highest accuracy of 96.47% and average accuracy of 95.97% over 10 runs of 10-fold CV.

A hybrid spatio-temporal model for detection and severity rating of Parkinson’s disease from gait data

“…For PD detection from gait data, machine learning methods, such as kernel Fisher discriminant, naïve Bayesian, and support vector machine, have been employed and achieved promising results [8][9][10][11][12][13][14][15]. However, these approaches only deal with it as a two-category classification problem, i.e.…”

Medical Data Classification Based on SMOTE and Recurrent Neural Network

“…Chen, et al [13] explore the performance of the Extreme Learning Machine (ELM) and Kernel ELM (KELM) method in the prediction of Parkinson's disease. The various parameters including kernel parameter, type of activation function in ELM and number of hidden neuron and constant parameter were analyzed in detailed.…”

Diagnosing Parkinson's Disease Using Movement Signal Mapping by Neural Network and Classifier Modulation

“…Chen and others [6] applied several feature-selecting methods: maximum relevance minimum redundancy (mRMR), relief information gain, and t-test. They also used two machine learning techniques-extreme learning machine (ELM) and kernel ELM (KELM)-to diagnose Parkinson's disease.…”

“…As values obtained from movement information are in a wide numeric range, data are normalized using the following formula to convert values to a narrower numeric range and improve efficiency [6]:…”