scholarly journals Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson’s Disease Using Voice Recordings

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 37718-37734 ◽  
Author(s):  
Amin Ul Haq ◽  
Jian Ping Li ◽  
Muhammad Hammad Memon ◽  
Jalaluddin khan ◽  
Asad Malik ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Recognition System ◽  
Support Vector ◽  
L1 Norm

scholarly journals Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images

Biomedicines ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 12
Author(s):  
Chung-Yao Chien ◽  
Szu-Wei Hsu ◽  
Tsung-Lin Lee ◽  
Pi-Shan Sung ◽  
Chou-Ching Lin
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Dopamine Transporter ◽  
Support Vector ◽  
Dat Spect ◽  
Ann Classifier ◽  
Spect Images

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

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.

Tremor Identification Using Machine Learning in Parkinson's Disease

2019 ◽  
pp. 128-151
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

scholarly journals Support Vector Machine Based Feature Extraction For Gender Recognition From Objects Using Lasso Classifier

2020 ◽  
Author(s):  
Damodara Krishna Kishore Galla ◽  
BabuReddy Mukamalla ◽  
Rama Prakasha Reddy Chegireddy
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Face Recognition ◽  
Selection Process ◽  
Recognition System ◽  
Classification Model ◽  
Support Vector ◽  
Gender Classification ◽  
Accuracy Score ◽  
Lasso Regression

Abstract The blind people has their difficulty to identify the object moving around them, therefore with a high accuracy score object detection and human face recognition system will helps them in identifying the things around them with ease. Facial record images are immobile an difficult assignment for biometric authentication systems due to various types of characteristics are dimensions, pose, expressions, illustrations and age etc. In facial and other united images includes different objects classifications. In this research article, a minimum distance trainer for feature selection by accessing SVM feature optimization process. For feature selection process SVM (support vector machine) was considered for improving its feature interpretability and computational efficiency., then LASSO classifier applied to perform object recognition and gender classification. Original face image database used for the gender classification. This approach was implemented with dual classification model (1) Recognizing or classifying human faces from various objects and (2) Classifying gender through face recognition] is made possible with the help of combining modified SIFT feature in combination with ridge regression (RR), elastic net (EN), lasso regression(LR) and lasso regression with Gaussian Support Vector Machines (LRGS) based classification.

Tremor Identification Using Machine Learning in Parkinson's Disease

2021 ◽  
pp. 341-365
Author(s):  
Angana Saikia ◽  
Vinayak Majhi ◽  
Masaraf Hussain ◽  
Sudip Paul ◽  
Amitava Datta
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Discriminant Analysis ◽  
Learning Algorithms ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector

Tremor is an involuntary quivering movement or shake. Characteristically occurring at rest, the classic slow, rhythmic tremor of Parkinson's disease (PD) typically starts in one hand, foot, or leg and can eventually affect both sides of the body. The resting tremor of PD can also occur in the jaw, chin, mouth, or tongue. Loss of dopamine leads to the symptoms of Parkinson's disease and may include a tremor. For some people, a tremor might be the first symptom of PD. Various studies have proposed measurable technologies and the analysis of the characteristics of Parkinsonian tremors using different techniques. Various machine-learning algorithms such as a support vector machine (SVM) with three kernels, a discriminant analysis, a random forest, and a kNN algorithm are also used to classify and identify various kinds of tremors. This chapter focuses on an in-depth review on identification and classification of various Parkinsonian tremors using machine learning algorithms.

scholarly journals Classification of Alzheimer’s disease and Parkinson’s disease using a support vector machine and probabilistic outputs

2017 ◽  
Vol 17 (0) ◽  
pp. 112-124
Author(s):  
Asuka Hatabu ◽  
Masafumi Harada ◽  
Yoshitake Takahashi ◽  
Shunsuke Watanabe ◽  
Kenya Sakamoto ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machine ◽  
Parkinson's Disease ◽  
Support Vector

A Methodology to Differentiate Parkinson’s Disease and Aging Speech Based on Glottal Flow Acoustic Analysis

2020 ◽  
Vol 30 (10) ◽  
pp. 2050058
Author(s):  
Andrés Gómez-Rodellar ◽  
Daniel Palacios-Alonso ◽  
José M. Ferrández Vicente ◽  
Jiri Mekyska ◽  
Agustín Álvarez-Marquina ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Acoustic Analysis ◽  
Support Vector ◽  
Healthy Controls ◽  
Male And Female ◽  
Highly Sensitive ◽  
Glottal Flow ◽  
Improved Accuracy

Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson’s Disease (PD). Patients suffering from PD present important alterations in speech, which are manifested in phonation, articulation, prosody, and fluency. These alterations may be evaluated using statistical methods on features obtained from glottal, spectral, cepstral, or fractal descriptions of speech. This work introduces an evaluation paradigm based on Information Theory (IT) to differentiate the effects of PD and aging on glottal amplitude distributions. The study is conducted on a database including 48 PD patients (24 males, 24 females), 48 age-matched healthy controls (HC, 24 males, 24 females), and 48 mid-age normative subjects (NS, 24 males, 24 females). It may be concluded from the study that Hierarchical Clustering (HiCl) methods produce a clear separation between the phonation of PD patients from NS subjects (accuracy of 89.6% for both male and female subsets), but the separation between PD patients and HC subjects is less efficient (accuracy of 75.0% for the male subset and 70.8% for the female subset). Conversely, using feature selection and Support Vector Machine (SVM) classification, the differentiation between PD and HC is substantially improved (accuracy of 94.8% for the male subset and 92.8% for the female subset). This improvement was mainly boosted by feature selection, at a cost of information and generalization losses. The results point to the possibility that speech deterioration may affect HC phonation with aging, reducing its difference to PD phonation.

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