An ensemble‐based feature selection framework for early detection of Parkinson's disease based on feature correlation analysis

2021 ◽  
Author(s):  
Sarfaraz Masood ◽  
Khwaja Wisal Maqsood ◽  
Om Pal ◽  
Chanchal Kumar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Early Detection ◽  
Correlation Analysis ◽  
Feature Correlation ◽  
Selection Framework

scholarly journals An ensemble feature selection framework for early detection of Parkinson's disease based on feature correlation analysis

2021 ◽  
Author(s):  
Sarfaraz Masood ◽  
Khwaja Wisal ◽  
Om Pal ◽  
Chanchal Kumar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Large Population ◽  
Feature Selection Method ◽  
Sampling Strategy ◽  
Chi Square ◽  
Accurate Identification ◽  
Initial Symptoms ◽  
Selection Framework

Parkinson’s disease (PD) is a highly common neurological disease affecting a large population worldwide. Several studies revealed that the degradation of voice is one of its initial symptoms, which is also known as dysarthria. In this work, we attempt to explore and harness the correlation between various features in the voice samples observed in PD subjects. To do so, a novel two-level ensemble-based feature selection method has been proposed, whose results were combined with an MLP based classifier using K-fold cross-validation as the re-sampling strategy. Three separate benchmark datasets of voice samples were used for the experimentation work. Results strongly suggest that the proposed feature selection framework helps in identifying an optimal set of features which further helps in highly accurate identification of PD patients using a Multi-Layer Perceptron from their voice samples. The proposed model achieves an overall accuracy of 98.3%, 95.1% and 100% on the three selected datasets respectively. These results are significantly better than those achieved by a non-feature selection based option, and even the recently proposed chi-square based feature selection option.

scholarly journals Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147635-147646 ◽  
Author(s):  
Wu Wang ◽  
Junho Lee ◽  
Fouzi Harrou ◽  
Ying Sun
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Detection

scholarly journals Kinematic and Kinetic Patterns Related to Free-Walking in Parkinson’s Disease

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4224 ◽  
Author(s):  
Martín Martínez ◽  
Federico Villagra ◽  
Juan Castellote ◽  
María Pastor
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Clinical Status ◽  
Automatic Classification ◽  
Healthy Controls ◽  
Double Support ◽  
Gait Asymmetry ◽  
Double Support Time ◽  
Support Time

The aim of this study is to compare the properties of free-walking at a natural pace between mild Parkinson’s disease (PD) patients during the ON-clinical status and two control groups. In-shoe pressure-sensitive insoles were used to quantify the temporal and force characteristics of a 5-min free-walking in 11 PD patients, in 16 young healthy controls, and in 12 age-matched healthy controls. Inferential statistics analyses were performed on the kinematic and kinetic parameters to compare groups’ performances, whereas feature selection analyses and automatic classification were used to identify the signature of parkinsonian gait and to assess the performance of group classification, respectively. Compared to healthy subjects, the PD patients’ gait pattern presented significant differences in kinematic parameters associated with bilateral coordination but not in kinetics. Specifically, patients showed an increased variability in double support time, greater gait asymmetry and phase deviation, and also poorer phase coordination. Feature selection analyses based on the ReliefF algorithm on the differential parameters in PD patients revealed an effect of the clinical status, especially true in double support time variability and gait asymmetry. Automatic classification of PD patients, young and senior subjects confirmed that kinematic predictors produced a slightly better classification performance than kinetic predictors. Overall, classification accuracy of groups with a linear discriminant model which included the whole set of features (i.e., demographics and parameters extracted from the sensors) was 64.1%.

scholarly journals A Simple and Effective Approach Based on a Multi-Level Feature Selection for Automated Parkinson’s Disease Detection

2022 ◽  
Vol 12 (1) ◽  
pp. 55
Author(s):  
Fatih Demir ◽  
Kamran Siddique ◽  
Mohammed Alswaitti ◽  
Kursat Demir ◽  
Abdulkadir Sengur
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Early Diagnosis ◽  
Neurodegenerative Disorder ◽  
Bayesian Optimization ◽  
L1 Norm ◽  
Bayesian Optimization Algorithm ◽  
Knn Classifier ◽  
Multi Level

Parkinson’s disease (PD), which is a slowly progressing neurodegenerative disorder, negatively affects people’s daily lives. Early diagnosis is of great importance to minimize the effects of PD. One of the most important symptoms in the early diagnosis of PD disease is the monotony and distortion of speech. Artificial intelligence-based approaches can help specialists and physicians to automatically detect these disorders. In this study, a new and powerful approach based on multi-level feature selection was proposed to detect PD from features containing voice recordings of already-diagnosed cases. At the first level, feature selection was performed with the Chi-square and L1-Norm SVM algorithms (CLS). Then, the features that were extracted from these algorithms were combined to increase the representation power of the samples. At the last level, those samples that were highly distinctive from the combined feature set were selected with feature importance weights using the ReliefF algorithm. In the classification stage, popular classifiers such as KNN, SVM, and DT were used for machine learning, and the best performance was achieved with the KNN classifier. Moreover, the hyperparameters of the KNN classifier were selected with the Bayesian optimization algorithm, and the performance of the proposed approach was further improved. The proposed approach was evaluated using a 10-fold cross-validation technique on a dataset containing PD and normal classes, and a classification accuracy of 95.4% was achieved.

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