Parkinson's Disease Dataset Research Articles

Deep Feature Extraction From the Vocal Vectors Using Sparse Autoencoders for Parkinson’s Classification

Parkinson’s disease (PD) is a chronic, neurodegenerative disorder that mainly affects the motor system of the body. The progression rate is very slow but, as the condition worsens it stimulates the non-motor characteristics, especially mood disorders, hallucinations, delusions, and other observable cognitive changes. Genetic and environmental factors greatly influence the risk of Parkinson’s development. The early diagnosis of PD improves the condition through proper treatment. During the initial stage of PD, vocal impairments become more common among the affected individuals. Advanced studies on vocal disorders give more assistance for precise PD detection. In this paper, the vocal features of PD affected individuals are analyzed with sophisticated computational models. Initially, the samples are pre-processed, as it contains more missing values. Then the predictor candidate subset is identified from the processed vocal features using the Adaptive Grey Wolf Optimization Algorithm, a meta-heuristic global search optimization technique. Furthermore, the latent representation of the candidate features is extracted through sparse autoencoders for effective discrimination between the PD affected and control cases. For classification, six supervised machine learning algorithms were employed. The proposed model is trained with the data, divided through 10-fold cross-validation scheme and the performance is evaluated based on validation metrics. The PD dataset is accessed from University of California (UCI), Irvine Machine Learning repository to conduct the experimental analysis. The result shows that the proposed algorithm outperformed the benchmarked models, exhibits its efficacy in distinguishing the affected and healthy samples of PD. The outcome of this study emphasizes the significance of intelligent learning models in complex disease diagnosis.

Lipid Metabolism is the common pathologic mechanism between Type 2 Diabetes Mellitus and Parkinson's disease.

Although increasing evidence has suggested crosstalk between Parkinson's disease (PD) and type 2 diabetes mellitus (T2DM), the common mechanisms between the two diseases remain unclear. The aim of our study was to characterize the interconnection between T2DM and PD by exploring their shared biological pathways and convergent molecules. The intersections among the differentially expressed genes (DEGs) in the T2DM dataset GSE95849 and PD dataset GSE6613 from the Gene Expression Omnibus (GEO) database were identified as the communal DEGs between the two diseases. Then, an enrichment analysis, protein-protein interaction (PPI) network analysis, correlation analysis, and transcription factor-target regulatory network analysis were performed for the communal DEGs. As a result, 113 communal DEGs were found between PD and T2DM. They were enriched in lipid metabolism, including protein modifications that regulate metabolism, lipid synthesis and decomposition, and the biological effects of lipid products. All these pathways and their biological processes play important roles in both diseases. Fifteen hub genes identified from the PPI network could be core molecules. Their function annotations also focused on lipid metabolism. According to the correlation analysis and the regulatory network analysis based on the 15 hub genes, Sp1 transcription factor (SP1) could be a key molecule since it affected other hub genes that participate in the common mechanisms between PD and T2DM. In conclusion, our analyses reveal that changes in lipid metabolism could be a key intersection between PD and T2DM, and that SP1 could be a key molecule regulating these processes. Our findings provide novel points for the association between PD and T2DM.

Automated Parkinson’s disease recognition based on statistical pooling method using acoustic features

Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods have been proposed to automatically diagnose Parkinson’s disease using acoustic, hand writing and gaits. In this study, a statistical pooling method is proposed to recognize Parkinson’s disease using the vowels. The used Parkinson's disease dataset contains the features of vowels. In the proposed method, the features of dataset are increased by applying statistical pooling method. Then, the most weighted features are selected from increased feature vector by using ReliefF. The classification is applied using the most weighted feature vector obtained. In the proposed method, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) algorithms are used. The success rate was calculated as 91.25% and 91.23% with by using SVM and KNN respectively. The proposed method has two main contributions. The first is to obtain new features from the Parkinson's acoustic dataset using the statistical pooling method. The second one is the selection of the most significant features from the many feature vectors obtained. Thus, successful results were obtained for both KNN and SVM algorithms. The comparatively results clearly show that the proposed method achieved the best success rate among the selected state-of-art methods. Considering the proposed method and the results obtained, it proposed method is successful for Parkinson’s disease recognition.

Deep Learning Approach to Parkinson's Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification.

This study presents an approach to Parkinson's disease detection using vowels with sustained phonation and a ResNet architecture dedicated originally to image classification. We calculated spectrum of the audio recordings and used them as an image input to the ResNet architecture pre-trained using the ImageNet and SVD databases. To prevent overfitting the dataset was strongly augmented in the time domain. The Parkinson's dataset (from PC-GITA database) consists of 100 patients (50 were healthy / 50 were diagnosed with Parkinson's disease). Each patient was recorded 3 times. The obtained accuracy on the validation set is above 90% which is comparable to the current state-of-the-art methods. The results are promising because it turned out that features learned on natural images are able to transfer the knowledge to artificial images representing the spectrogram of the voice signal. What is more, we showed that it is possible to perform a successful detection of Parkinson's disease using only frequency-based features. A spectrogram enables visual representation of frequencies spectrum of a signal. It allows to follow the frequencies changes of a signal in time.

An adaptive fuzzy K-nearest neighbor approach for MR brain tumor image classification using parameter free bat optimization algorithm

This paper presents an automatic diagnosis system for the tumor grade classification through magnetic resonance imaging (MRI). The diagnosis system involves a region of interest (ROI) delineation using intensity and edge magnitude based multilevel thresholding algorithm. Then the intensity and the texture attributes are extracted from the segregated ROI. Subsequently, a combined approach known as Fisher+ Parameter-Free BAT (PFreeBAT) optimization is employed to derive the optimal feature subset. Finally, a novel learning approach dubbed as PFree BAT enhanced fuzzy K-nearest neighbor (FKNN) is proposed by combining FKNN with PFree BAT for the classification of MR images into two categories: High and Low-Grade. In PFree BAT enhanced FKNN, the model parameters, i.e., neighborhood size k and the fuzzy strength parameter m are adaptively specified by the PFree BAT optimization approach. Integrating PFree BAT with FKNN enhances the classification capability of the FKNN. The diagnostic system is rigorously evaluated on four MR images datasets including images from BRATS 2012 database and the Harvard repository using classification performance metrics. The empirical results illustrate that the diagnostic system reached to ceiling level of accuracy on the test MR image dataset via 5-fold cross-validation mechanism. Additionally, the proposed PFree BAT enhanced FKNN is evaluated on the Parkinson dataset (PD) from the UCI repository having the pre-extracted feature space. The proposed PFree BAT enhanced FKNN reached to an average accuracy of 98% and 97.45%. with and without feature selection on PD dataset. Moreover, solely to contrast, the performance of the proposed PFree BAT enhanced FKNN with the existing FKNN variants the experimentations were also done on six other standard datasets from KEEL repository. The results indicate that the proposed learning strategy achieves the best value of accuracy in contrast to the existing FKNN variants.

An analytical method for measuring the Parkinson’s disease progression: A case on a Parkinson’s telemonitoring dataset

The use of machine learning techniques for early diseases diagnosis has attracted the attention of scholars worldwide. Parkinson’s Disease (PD) is one of themost common neurological and complicated diseases affecting the central nervous system. Unified Parkinson’s Disease Rating Scale (UPDRS) is widely used for tracking PD symptom progression. Motor- and Total-UPDRS are two important clinical scales of PD. The aim of this study is to predict UPDRS scores through analyzing the speech signal properties which is important in PD diagnosis. We take the advantages of ensemble learning and dimensionality reduction techniques and develop a new hybrid method to predict Total- and Motor-UPDRS. We accordingly improve the time complexity and accuracy of the PD diagnosis systems, respectively, by using Singular Value Decomposition (SVD) and ensembles of Adaptive Neuro-Fuzzy Inference System (ANFIS). We evaluate our method on a large PD dataset and present the results. The results showed that the proposed method is effective in predicting PD progression by improving the accuracy and computation time of the disease diagnosis. The method can be implemented as a medical decision support system for real-time PD diagnosis when big data from the patients is available in the medical datasets.

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

The patient of Parkinson’s disease (PD) is facing a critical neurological disorder issue. Efficient and early prediction of people having PD is a key issue to improve patient’s quality of life. The diagnosis of PD specifically in its initial stages is extremely complex and time-consuming. Thus, the accurate and efficient diagnosis of PD has been a significant challenge for medical experts and practitioners. In order to tackle this issue and to accurately diagnosis the patient of PD, we proposed a machine-learning-based prediction system. In the development of the proposed system, the support vector machine (SVM) was used as a predictive model for the prediction of PD. The L1-norm SVM of features selection was used for appropriate and highly related features selection for accurate target classification of PD and healthy people. The L1-norm SVM produced a new subset of features from the PD dataset based on a feature weight value. For the validation of the proposed system, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -fold cross-validation method was used. In addition, the metrics of performance measures, such as accuracy, sensitivity, specificity, precision, F1 score, and execution time, were computed for model performance evaluation. The PD dataset was in this paper. The optimal accuracy achieved the best subset of the selected features that might be due to various contributions of the PD features. The experimental findings of this paper suggest that the proposed method can be used to accurately predict the PD and can be easily incorporated in healthcare for diagnosis purpose. Currently, the computer-based assisted predictive system is playing an important role to assist in PD recognition. In addition, the proposed approach fills in a gap on feature selection and classification using voice recordings data by properly matching the experimental design.

Parkinson's diagnosis using ant-lion optimisation algorithm

Parkinson's disease (PD) is a long term progressive disorder of the central nervous system that mainly affects the movement of the body. But there are several limitations in detecting PD at an early stage. In this paper, a binary variant of the recently proposed ant-lion optimisation (ALO) algorithm has been proposed and implemented for diagnosing patients for Parkinson's disease at early stages. ALO is a recently proposed bio-inspired algorithm, which imitates the hunting patterns of ant-lions or doodlebugs proposed algorithm is used to find a minimum number of features that result in higher accuracy using machine learning classifiers. The proposed modified version of ALO extracts the optimal features for the two different Parkinson's Datasets with improved accuracy and computational time. The maximum accuracy achieved by the classifiers after optimal feature selection is 95.91%. The proposed algorithm results have been compared with other related algorithms for the same datasets.

Neuroimaging-based diagnosis of Parkinson's disease with deep neural mapping large margin distribution machine

Abstract Neuroimaging has shown its effectiveness for diagnosis of Parkinson's disease (PD), and the neuroimaging-based computer-aided diagnosis (CAD) then attracts considerable attention. In a CAD system, the classifier module is one of the key components, which directly decides the classification performance. As a newly proposed classifier, the large margin distribution machine (LDM) has excellent generalization by maximizing the margin mean and minimizing the margin variance simultaneously. However, LDM still suffers from the problem of kernel selection. In this work, we propose a deep neural mapping large margin distribution machine (DNMLDM) algorithm by adopting the deep neural network (DNN) to perform a kernel mapping instead of the implicit kernel function in LDM. A two-stage joint training strategy is then developed, including the unsupervised layer-wise pre-training for DNN and then the supervised fine-tuning for all parameters in the whole networks. Two real-world PD datasets, namely the transcranial sonography (TCS) dataset and the magnetic resonance imaging (MRI) dataset, are used to evaluate the performance of DNMLDM algorithm. The experimental results show that the proposed DNMLDM outperforms all the compared algorithms on both datasets.

An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach.

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.

The Problem of Correction Diagnostic Errors in the Target Attribute With the Function of Rival Similarity

The problem of outliers detection is one of the important problems in Data Mining of biomedical datasets particularly in case when there could be misclassified objects, caused by diagnostic pitfalls on a stage of a data collection. Occurrence of such objects complicates and slows down dataset processing, distorts and corrupts detected regularities, reduces their accuracy score. We propose the censoring algorithm which could detect misclassified objects after which they are either removed from the dataset or the class attribute of such objects is corrected. Correction procedure keeps the volume of the analyzed dataset as big as it is possible. Such quality is very useful in case of small datasets analysis, when every bit of information can be important. The base concept in the presented work is a measure of similarity of objects with its surroundings. To evaluate the local similarity of the object with its closest neighbors the ternary relative measure called the function of rival similarity (FRiS-function) is used. Mean of similarity values of all objects in the dataset gives us a notion of a class’s separability, how close objects from the same class are to each other and how far they are from the objects of the different classes (with the different diagnosis) in the attribute space. It is supposed misclassified objects are more similar to objects from rival classes, than their own class, so their elimination from the dataset, or the target attribute correction should increase data separability value. The procedure of filtering-correcting of misclassified objects is based on the observation of changes in the evaluation of data separability calculated before and after making corrections to the dataset. The censoring process continues until the inflection point of the separability function is reached. The proposed algorithm was tested on a wide range of model tasks of different complexity. Also it was tested on biomedical tasks such as Pima Indians Diabetes data set, Breast Cancer data set and Parkinson data set. On these tasks the censoring algorithm showed high misclassification sensitivity. Accuracy score increasing and data set volume preservation after censoring procedure proved our base assumptions and the effectiveness of the algorithm.

A hybrid intelligent system for the prediction of Parkinson's Disease progression using machine learning techniques

Parkinson's Disease (PD) is a progressive degenerative disease of the nervous system that affects movement control. Unified Parkinson's Disease Rating Scale (UPDRS) is the baseline assessment for PD. UPDRS is the most widely used standardized scale to assess parkinsonism. Discovering the relationship between speech signal properties and UPDRS scores is an important task in PD diagnosis. Supervised machine learning techniques have been extensively used in predicting PD through a set of datasets. However, the most methods developed by supervised methods do not support the incremental updates of data. In addition, the standard supervised techniques cannot be used in an incremental situation for disease prediction and therefore they require to recompute all the training data to build the prediction models. In this paper, we take the advantages of an incremental machine learning technique, Incremental support vector machine, to develop a new method for UPDRS prediction. We use Incremental support vector machine to predict Total-UPDRS and Motor-UPDRS. We also use Non-linear iterative partial least squares for data dimensionality reduction and self-organizing map for clustering task. To evaluate the method, we conduct several experiments with a PD dataset and present the results in comparison with the methods developed in the previous research. The prediction accuracies of method measured by MAE for the Total-UPDRS and Motor-UPDRS were obtained respectively MAE=0.4656 and MAE=0.4967. The results of experimental analysis demonstrated that the proposed method is effective in predicting UPDRS. The method has potential to be implemented as an intelligent system for PD prediction in healthcare.

Feature Selection and Radial Basis Function Network for Parkinson Disease Classification

Recently, several works have focused on detection of a different disease using computational intelligence techniques. In this paper, we applied feature selection method and radial basis function neural network (RBFN) to classify the diagnosis of Parkinson’s disease. The feature selection (FS) method used to reduce the number of attributes in Parkinson disease data. The Parkinson disease dataset is acquired from UCI repository of large well-known data sets. The experimental results have revealed significant improvement to detect Parkinson’s disease using feature selection method and RBF network.

Feature selection and extraction for class prediction in dysphonia measures analysis:A case study on Parkinson's disease speech rehabilitation.

Speech disorders such as dysphonia and dysarthria represent an early and common manifestation of Parkinson's disease. Class prediction is an essential task in automatic speech treatment, particularly in the Parkinson's disease case. Many classification experiments have been performed which focus on the automatic detection of Parkinson's disease patients from healthy speakers but results are still not optimistic. A major problem in accomplishing this task is high dimensionality of speech data. In this work, the potential of Principal Component Analysis (PCA) based modeling in dimensionality reduction is taken into consideration as the data smoothening tool with multiclass target expression data. On the basis of suggested PCA-based modeling, the power of class prediction using logistic regression (LR) and C5.0in numeric data is investigated in publicly available Parkinson's disease dataset Silverman voice treatment (LSVT) to develop an advanced classification model. The main advantage of our model is the effective reduction of the number of factors from p= 309 to k= 32 for LSVT Voice Rehabilitation dataset, with a fine classification accuracy of 100% and 99.92% for PCA-LR and PCA-C5.0 respectively. In addition, using only 9 dysphonia features, classification accuracy was (99.20%) and (99.11%) for PCA-LR, and PCA-C5.0 respectively. Our combined dimension reduction and data smoothening approaches have significant potential to minimize the number of features and increase the classification accuracy and then automatically classify subjects into Parkinson's disease patients or healthy speakers.

An adaptive kernel-based weighted extreme learning machine approach for effective detection of Parkinson’s disease

Imbalanced data appear in many real-world applications, from biomedical application to network intrusion or fraud detection, etc. Existing methods for Parkinson’s disease (PD) diagnosis are usually more concerned with overall accuracy (ACC), but ignore the classification performance of the minority class. To alleviate the bias against performance caused by imbalanced data, in this paper, an effective method named AABC-KWELM has been proposed for PD detection. First, based on a fast classifier extreme learning machine (ELM), weighted strategy is used for dealing with imbalanced data and non-linear mapping of kernel function is used for improving the extent of linear separation. Furthermore, both binary version and continuous version of an adaptive artificial bee colony (AABC) algorithm are used for performing feature selection and parameters optimization, respectively. Finally, PD data set is used for evaluating rigorously the effectiveness of the proposed method in accordance with specificity, sensitivity, ACC, G-mean and F-measure. Experimental results demonstrate that the proposed AABC-KWELM remarkably outperforms other approaches in the literature and obtains better classification performance via 5-fold cross-validation (CV), with specificity of 100%, sensitivity of 98.62%, ACC of 98.97%, G-mean of 99.30%, and F-measure of 99.30%.

Comparative Analysis of Weighted Emphirical Optimization Algorithm and Lazy Classification Algorithms

Health care has millions of centric data to discover the essential data is more important. In data mining the discovery of hidden information can be more innovative and useful for much necessity constraint in the field of forecasting, patient’s behavior, executive information system, e-governance the data mining tools and technique play a vital role. In Parkinson health care domain the hidden concept predicts the possibility of likelihood of the disease and also ensures the important feature attribute. The explicit patterns are converted to implicit by applying various algorithms i.e., association, clustering, classification to arrive at the full potential of the medical data. In this research work Parkinson dataset have been used with different classifiers to estimate the accuracy, sensitivity, specificity, kappa and roc characteristics.

InSyBio BioNets: an efficient tool for network-based biomarker discovery

Biological networks have been widely used in systems biology in order to model the complex interactions of molecular players such as proteins, genes, mRNAs, non-coding RNAs and others. However, most of the current methods for biomarker discovery do not use biological networks, but just deploy simple statistical methods to identify differentially expressed genes and gene products. In the present paper, we present InSyBio BioNets, which is a cloud-based web platform offering a unique biomarker discovery pipeline, which combines differential expression analysis and a method for comparing biological networks to identify biomarkers efficiently. As a case study, InSyBio BioNets was applied to a Parkinson dataset of gene expression measurements and outperformed a standard statistical approach by recovering a more compact and informative set of biomarkers.

Joint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data

Parkinson's disease (PD) is an overwhelming neurodegenerative disorder caused by deterioration of a neurotransmitter, known as dopamine. Lack of this chemical messenger impairs several brain regions and yields various motor and non-motor symptoms. Incidence of PD is predicted to double in the next two decades, which urges more research to focus on its early diagnosis and treatment. In this paper, we propose an approach to diagnose PD using magnetic resonance imaging (MRI) data. Specifically, we first introduce a joint feature-sample selection (JFSS) method for selecting an optimal subset of samples and features, to learn a reliable diagnosis model. The proposed JFSS model effectively discards poor samples and irrelevant features. As a result, the selected features play an important role in PD characterization, which will help identify the most relevant and critical imaging biomarkers for PD. Then, a robust classification framework is proposed to simultaneously de-noise the selected subset of features and samples, and learn a classification model. Our model can also de-noise testing samples based on the cleaned training data. Unlike many previous works that perform de-noising in an unsupervised manner, we perform supervised de-noising for both training and testing data, thus boosting the diagnostic accuracy. Experimental results on both synthetic and publicly available PD datasets show promising results. To evaluate the proposed method, we use the popular Parkinson's progression markers initiative (PPMI) database. Our results indicate that the proposed method can differentiate between PD and normal control (NC), and outperforms the competing methods by a relatively large margin. It is noteworthy to mention that our proposed framework can also be used for diagnosis of other brain disorders. To show this, we have also conducted experiments on the widely-used ADNI database. The obtained results indicate that our proposed method can identify the imaging biomarkers and diagnose the disease with favorable accuracies compared to the baseline methods.

A Machine Learning System for the Diagnosis of Parkinson’s Disease from Speech Signals and Its Application to Multiple Speech Signal Types

Parkinson’s disease (PD) is a neurodegenerative disorder that negatively affects millions of people. PD is usually diagnosed by a series of empirical tests and sometimes by invasive methods. Classifying People with Parkinsonism (PWP) from healthy people using speech signals may lead to innovative, noninvasive PD diagnosis. In this study, we developed a machine learning system to classify PWP using their speech signals. In the system, four feature selection algorithms, six classifiers, and two validation methods were employed for accurate classification of PWP. The system calculated the accuracy, sensitivity, specificity, and Matthews correlation coefficient of the results. Additionally, the execution times of the algorithms were computed. All utilized algorithms, classifiers, validation methods, and evaluation metrics are briefly reviewed in the article. The main innovative part of this study is developing a comprehensive machine learning system for classifying PWP and testing it on a PD dataset, which consisted of multiple types of speech signals. Applying feature selection methods greatly increased the accuracy of classification. The most significant and discriminative features of speech signals were obtained and explained with a medical background. The importance of the selected features is also evaluated from the medical perspective.

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

Parkinson’s disease (PD) is a progressive and chronic nervous system disease that impairs the ability of speech, gait, and complex muscle-and-nerve actions. Early diagnosis of PD is quite important for alleviating the symptoms. Cost effective and convenient telemedicine technology helps to distinguish the patients with PD from healthy people using variations of dysphonia, gait or motor skills. In this study, a novel telemedicine technology was developed to detect PD remotely using dysphonia features. Feature transformation and several machine learning (ML) methods with 2-, 5- and 10-fold cross-validations were implemented on the vocal features. It was observed that the combination of principal component analysis (PCA) as a feature transformation (FT) and k-nearest neighbor (k-NN) as a classifier with 10-fold cross-validation has the best accuracy as 99.1%. All ML processes were applied to the prerecorded PD dataset using a newly created program named ParkDet 2.0. Additionally, the blind test interface was created on the ParkDet so that users could detect new patients with PD in future. Clinicians or medical technicians, without any knowledge of ML, will be able to use the blind test interface to detect PD at a clinic or remote location utilizing internet as a telemedicine application.