An intelligent system for Parkinson’s diagnosis using hybrid feature selection approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Intelligent System ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Feature Reduction ◽  
Selection Methods ◽  
Selection Approach ◽  
Feature Selection Approach

Parkinson’s is the second most common neurodegenerative disorder after Alzheimer’s disease which adversely affects the nervous system of the patients. During the nascent stage, the symptoms of Parkinson’s disease are mild and sometimes go unnoticeable but as the disease progresses the symptoms go severe, so its diagnosis at an early stage is not easy. Recent research has shown that changes in speech or distortion in voice can be taken effectively used for early Parkinson’s detection. In this work, the authors propose a system of Parkinson's disease detection using speech signals. As the feature selection plays an important role during classification, authors have proposed a hybrid MIRFE feature selection approach. The result of the proposed feature selection approach is compared with the 5 standard feature selection methods by XGBoost classifier. The proposed MIRFE approach selects 40 features out of 754 features with a feature reduction ratio of 94.69%. An accuracy of 93.88% and area under curve (AUC) of 0.978 is obtained by the proposed system.

Recognition of the parkinson’s disease using a hybrid feature selection approach

2020 ◽  
Vol 39 (1) ◽  
pp. 1319-1339
Author(s):  
Amin Ul Haq ◽  
Jianping Li ◽  
Muhammad Hammad Memon ◽  
Jalaluddin khan ◽  
Zafar Ali ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Selection Approach ◽  
Feature Selection Approach

scholarly journals Plasma-borne indicators of inflammasome activity in Parkinson’s disease patients

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Faith L. Anderson ◽  
Katharine M. von Herrmann ◽  
Angeline S. Andrew ◽  
Yuliya I. Kuras ◽  
Alison L. Young ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cell Death ◽  
Nlrp3 Inflammasome ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Inflammasome Activation ◽  
Membrane Pore ◽  
Related Proteins ◽  
Inflammasome Activity

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms and loss of dopaminergic neurons of the substantia nigra. Inflammation and cell death are recognized aspects of PD suggesting that strategies to monitor and modify these processes may improve the management of the disease. Inflammasomes are pro-inflammatory intracellular pattern recognition complexes that couple these processes. The NLRP3 inflammasome responds to sterile triggers to initiate pro-inflammatory processes characterized by maturation of inflammatory cytokines, cytoplasmic membrane pore formation, vesicular shedding, and if unresolved, pyroptotic cell death. Histologic analysis of tissues from PD patients and individuals with nigral cell loss but no diagnosis of PD identified elevated expression of inflammasome-related proteins and activation-related “speck” formation in degenerating mesencephalic tissues compared with controls. Based on previous reports of circulating inflammasome proteins in patients suffering from heritable syndromes caused by hyper-activation of the NLRP3 inflammasome, we evaluated PD patient plasma for evidence of inflammasome activity. Multiple circulating inflammasome proteins were detected almost exclusively in extracellular vesicles indicative of ongoing inflammasome activation and pyroptosis. Analysis of plasma obtained from a multi-center cohort identified elevated plasma-borne NLRP3 associated with PD status. Our findings are consistent with others indicating inflammasome activity in neurodegenerative disorders. Findings suggest mesencephalic inflammasome protein expression as a histopathologic marker of early-stage nigral degeneration and suggest plasma-borne inflammasome-related proteins as a potentially useful class of biomarkers for patient stratification and the detection and monitoring of inflammation in PD.

Olfactory Function and Diffusion Tensor Imaging as Markers of Mild Cognitive Impairment in Early Stages of Parkinson's Disease

2021 ◽  
pp. 155005942110582
Author(s):  
Sophie A. Stewart ◽  
Laura Pimer ◽  
John D. Fisk ◽  
Benjamin Rusak ◽  
Ron A. Leslie ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Diffusion Tensor Imaging ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Neurodegenerative Disorder ◽  
Diffusion Tensor ◽  
Early Stage ◽  
White Matter Integrity

Parkinson's disease (PD) is a neurodegenerative disorder that is typified by motor signs and symptoms but can also lead to significant cognitive impairment and dementia Parkinson's Disease Dementia (PDD). While dementia is considered a nonmotor feature of PD that typically occurs later, individuals with PD may experience mild cognitive impairment (PD-MCI) earlier in the disease course. Olfactory deficit (OD) is considered another nonmotor symptom of PD and often presents even before the motor signs and diagnosis of PD. We examined potential links among cognitive impairment, olfactory functioning, and white matter integrity of olfactory brain regions in persons with early-stage PD. Cognitive tests were used to established groups with PD-MCI and with normal cognition (PD-NC). Olfactory functioning was examined using the University of Pennsylvania Smell Identification Test (UPSIT) while the white matter integrity of the anterior olfactory structures (AOS) was examined using magnetic resonance imaging (MRI) diffusion tensor imaging (DTI) analysis. Those with PD-MCI demonstrated poorer olfactory functioning and abnormalities based on all DTI parameters in the AOS, relative to PD-NC individuals. OD and microstructural changes in the AOS of individuals with PD may serve as additional biological markers of PD-MCI.

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.

scholarly journals A Composite Hybrid Feature Selection Learning-Based Optimization of Genetic Algorithm For Breast Cancer Detection

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Selim ◽  
Hatem Khater
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Early Stage ◽  
Fitness Function ◽  
Support Vector ◽  
Initial Population ◽  
Tree Classifier ◽  
Selection Approach ◽  
Feature Selection Approach

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

scholarly journals Prediction of Parkinson’s Disease at Early Stage using Big Data Analytics

2020 ◽  
Vol 9 (4) ◽  
pp. 2453-2459
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Big Data ◽  
Data Analytics ◽  
Clinical Medicine ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Big Data Analytics ◽  
Analytical Techniques ◽  
Predictive Methods

Due to technological improvements in healthcare industry and clinical medicine, it requires to adapt new software techniques and tools to predict, diagnose and analyze disease patterns for making decisions in the early stage of disease. Parkinson’s disease is a neurodegenerative disorder. The PD damage the motor skills and may create speech problem and also affect the decision making process. Many people suffers with PD all over the world from many years. Day by day, the PD data has been increased, so the existing data mining predictive methods and tools does not give accurate results early for making decisions by doctors to save and increase the patient life period. Early PD symptoms can be detected by Big Data Analytics and proper medicine will be provided at the right time. In this paper, we are doing survey of predictive methods, Big Data Analytical techniques and also earlier researchers results presented.

scholarly journals Semantic Memory and Lexical Availability in Parkinson’s Disease: A Statistical Learning Study

2021 ◽  
Vol 13 ◽  
Author(s):  
Juan F. Cardona ◽  
Johan S. Grisales-Cardenas ◽  
Catalina Trujillo-Llano ◽  
Jesús A. Diazgranados ◽  
Hugo F. Urquina ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Semantic Memory ◽  
Classification Accuracy ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Semantic Fluency ◽  
Semantic Categories ◽  
Semantic Fields ◽  
Living Things

Parkinson’s disease (PD) is a neurodegenerative disorder that causes a progressive impairment in motor and cognitive functions. Although semantic fluency deficits have been described in PD, more specific semantic memory (SM) and lexical availability (LA) domains have not been previously addressed. Here, we aimed to characterize the cognitive performance of PD patients in a set of SM and LA measures and determine the smallest set of neuropsychological (lexical, semantic, or executive) variables that most accurately classify groups. Thirty early-stage non-demented PD patients (age 35–75, 10 females) and thirty healthy controls (age 36–76, 12 females) were assessed via general cognitive, SM [three subtests of the CaGi battery including living (i.e., elephant) and non-living things (i.e., fork)], and LA (eliciting words from 10 semantic categories related to everyday life) measures. Results showed that PD patients performed lower than controls in two SM global scores (picture naming and naming in response to an oral description). This impairment was particularly pronounced in the non-living things subscale. Also, the number of words in the LA measure was inferior in PD patients than controls, in both larger and smaller semantic fields, showing a more inadequate recall strategy. Notably, the classification algorithms indicated that the SM task had high classification accuracy. In particular, the denomination of non-living things had a classification accuracy of ∼80%. These results suggest that frontostriatal deterioration in PD leads to search strategy deficits in SF and the potential disruption in semantic categorization. These findings are consistent with the embodied view of cognition.

A Feature Selection Approach for Network Intrusion Classification

2013 ◽  
Vol 3 (4) ◽  
pp. 51-59 ◽  
Author(s):  
Heba F. Eid ◽  
Mostafa A. Salama ◽  
Aboul Ella Hassanien
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Local Maximum ◽  
Selection Methods ◽  
Hybrid Filter ◽  
Data Set ◽  
Network Intrusion ◽  
Selection Approach ◽  
Feature Selection Approach

Feature selection is a preprocessing step to machine learning, leads to increase the classification accuracy and reduce its complexity. Feature selection methods are classified into two main categories: filter and wrapper. Filter methods evaluate features without involving any learning algorithm, while wrapper methods depend on a learning algorithm for feature evaluation. Variety hybrid Filter and wrapper methods have been proposed in the literature. However, hybrid filter and wrapper approaches suffer from the problem of determining the cut-off point of the ranked features. This leads to decrease the classification accuracy by eliminating important features. In this paper the authors proposed a Hybrid Bi-Layer behavioral-based feature selection approach, which combines filter and wrapper feature selection methods. The proposed approach solves the cut-off point problem for the ranked features. It consists of two layers, at the first layer Information gain is used to rank the features and select a new set of features depending on a global maxima classification accuracy. Then, at the second layer a new subset of features is selected from within the first layer redacted data set by searching for a group of local maximum classification accuracy. To evaluate the proposed approach it is applied on NSL-KDD dataset, where the number of features is reduced from 41 to 34 features at the first layer. Then reduced from 34 to 20 features at the second layer, which leads to improve the classification accuracy to 99.2%.

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