scholarly journals Data-Driven Based Approach to Aid Parkinson’s Disease Diagnosis

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 242 ◽  
Author(s):  
Nicolas Khoury ◽  
Ferhat Attal ◽  
Yacine Amirat ◽  
Latifa Oukhellou ◽  
Samer Mohammed
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Performance Evaluation ◽  
Random Forest ◽  
Neurodegenerative Diseases ◽  
Healthy Subjects ◽  
Support Vector ◽  
Classification Methods ◽  
Data Set ◽  
Feature Extraction And Selection

This article presents a machine learning methodology for diagnosing Parkinson’s disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model (GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collected within three different studies is used. This data set includes vGRF measurements collected from eight force sensors placed under each foot of the subjects. Ninety-three patients suffering from Parkinson’s disease and 72 healthy subjects participated in the experiments. The obtained performances are compared with respect to various metrics including accuracy, precision, recall and F-measure. The classification performance evaluation is performed using the leave-one-out cross validation. The results demonstrate the ability of the proposed methodology to accurately differentiate between PD subjects and healthy subjects. For the purpose of validation, the proposed methodology is also evaluated with an additional dataset including subjects with neurodegenerative diseases (Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD)). The obtained results show the effectiveness of the proposed methodology to discriminate PD subjects from subjects with other neurodegenerative diseases with a relatively high accuracy.

scholarly journals Can Gut Microbiota Be a Good Predictor for Parkinson’s Disease? A Machine Learning Approach

2020 ◽  
Vol 10 (4) ◽  
pp. 242 ◽  
Author(s):  
Daniele Pietrucci ◽  
Adelaide Teofani ◽  
Valeria Unida ◽  
Rocco Cerroni ◽  
Silvia Biocca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Gut Microbiota ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Published Data ◽  
Promising Tool

The involvement of the gut microbiota in Parkinson’s disease (PD), investigated in several studies, identified some common alterations of the microbial community, such as a decrease in Lachnospiraceae and an increase in Verrucomicrobiaceae families in PD patients. However, the results of other bacterial families are often contradictory. Machine learning is a promising tool for building predictive models for the classification of biological data, such as those produced in metagenomic studies. We tested three different machine learning algorithms (random forest, neural networks and support vector machines), analyzing 846 metagenomic samples (472 from PD patients and 374 from healthy controls), including our published data and those downloaded from public databases. Prediction performance was evaluated by the area under curve, accuracy, precision, recall and F-score metrics. The random forest algorithm provided the best results. Bacterial families were sorted according to their importance in the classification, and a subset of 22 families has been identified for the prediction of patient status. Although the results are promising, it is necessary to train the algorithm with a larger number of samples in order to increase the accuracy of the procedure.

scholarly journals A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson’s Disease Using Wearable Based Gait Signals

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 421
Author(s):  
Satyabrata Aich ◽  
Jinyoung Youn ◽  
Sabyasachi Chakraborty ◽  
Pyari Mohan Pradhan ◽  
Jin-han Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Wearable Devices ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Healthcare Applications ◽  
Reported Data

Fluctuations in motor symptoms are mostly observed in Parkinson’s disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the “On”/“Off” state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%.

PRESENTING A NEW DECISION SUPPORT SYSTEM FOR SCREENING PARKINSON’S DISEASE PATIENTS USING SYMLET WAVELET

2019 ◽  
Vol 31 (04) ◽  
pp. 1950026
Author(s):  
Yashar Sarbaz ◽  
Behzad Abedi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Decision Support ◽  
Healthy Subjects ◽  
Disease Diagnosis ◽  
Gait Disorders ◽  
Support Vector ◽  
Double Support ◽  
Number Of Patients ◽  
Classifier Performance

Objective: Parkinson’s Disease (PD) is a neurodegenerative disease that is categorized by tremor, rigidity, and bradykinesia. Currently, there is no standard method to diagnose patients with PD. One of the common symptoms of PD is gait disorders which are caused by rigid muscles. Gait disorders may start some years before disease diagnosis. Therefore, better understanding of the gait signal can lead to early diagnosis of PD. Methods: Computer-aided system has been useful in early detection of PD symptoms. In the present study, gait disturbances have received attention as potential biomarkers for early diagnosing of PD. Time and frequency analysis of gait signals together can provide more useful information. Wavelet-based features were extracted from stride, swing and double support time signals of healthy subjects and PD patients. These signals were decomposed into five levels using “sym4” wavelet. Mean and standard deviation (SD) of the absolute values of the approximation and detailed coefficients at each level were computed. Then final features were picked accordingly to obtain the best result for the classification. Results: Support Vector Machine (SVM) was employed for classification of patients and healthy people. The classifier performance was measured based on accuracy, sensitivity and specificity. The classifier performance is obtained with 93.3% accuracy employing linear kernel. Conclusions: The proposed system can be employed as a Decision Support Systems (DSSs) for early diagnosing of PD. Presenting DSSs can be employed to screen suspected cases of PD disease for further evaluation. Studying large number of patients and healthy subjects may lead to more precise study on PD. Also, it seems that using other different classifiers, along with our features, can reduce the diagnosis error.

scholarly journals Estimation of Precipitation Area Using S-Band Dual-Polarization Radar Measurements

2021 ◽  
Vol 13 (11) ◽  
pp. 2039
Author(s):  
Joon Jin Song ◽  
Melissa Innerst ◽  
Kyuhee Shin ◽  
Bo-Young Ye ◽  
Minho Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Support Vector ◽  
Classification Methods ◽  
Dual Polarization ◽  
Data Set ◽  
Spatial Classification ◽  
Precipitation Area ◽  
Radar Measurements

Estimating precipitation area is important for weather forecasting as well as real-time application. This paper aims to develop an analytical framework for efficient precipitation area estimation using S-band dual-polarization radar measurements. Several types of factors, such as types of sensors, thresholds, and models, are considered and compared to form a data set. After building the appropriate data set, this paper yields a rigorous comparison of classification methods in statistical (logistic regression and linear discriminant analysis) and machine learning (decision tree, support vector machine, and random forest). To achieve better performance, spatial classification is considered by incorporating latitude and longitude of observation location into classification, compared with non-spatial classification. The data used in this study were collected by rain detector and present weather sensor in a network of automated weather systems (AWS), and an S-band dual-polarimetric weather radar during ten different rainfall events of varying lengths. The mean squared prediction error (MSPE) from leave-one-out cross validation (LOOCV) is computed to assess the performance of the methods. Of the methods, the decision tree and random forest methods result in the lowest MSPE, and spatial classification outperforms non-spatial classification. Particularly, machine-learning-based spatial classification methods accurately estimate the precipitation area in the northern areas of the study region.

scholarly journals A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease

2019 ◽  
Vol 19 (S9) ◽  
Author(s):  
Domenico Buongiorno ◽  
Ilaria Bortone ◽  
Giacomo Donato Cascarano ◽  
Gianpaolo Francesco Trotta ◽  
Antonio Brunetti ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Healthy Subjects ◽  
Intelligent Systems ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Microsoft Kinect ◽  
Support Vector ◽  
Medical Specialists ◽  
Foot Tapping

Abstract Background Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. Methods In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution. Results Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). Conclusions The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Qualitative Analysis for Improving Prediction Accuracy in Parkinson's Disease Detection Using Hybrid Technique

2019 ◽  
Vol 16 (2) ◽  
pp. 393-399 ◽  
Author(s):  
S. Geeitha ◽  
M. Thangamani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Expectation Maximization ◽  
Prediction Accuracy ◽  
Gene Expression Pattern ◽  
Support Vector ◽  
Hybrid Technique ◽  
Computational Tool ◽  
Bayesian Network Model ◽  
Data Set

A PSO based SVM method has been implemented in diagnosing Parkinson's disease. This hybrid method produces parameter optimization and it helps to predict the gene expression pattern of the patient affected from Parkinson's disease. Implementing a computational tool on the PD data set alleviates the symptoms to predict accurately the occurrence of the disease. In data classification, there may arise some incomplete or missing data during pre-processing in the probabilistic model. In order to overcome this, an Expectation Maximization (EM) algorithm is implemented. The proposed Particle Swarm Optimization (PSO) based Support Vector Machine (SVM) technique is also compared with the Bayesian network model that outperforms in prediction accuracy.

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