Hybrid Machine Learning Classifier and Ensemble Techniques to Detect Parkinson’s Disease Patients

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Sunil Yadav ◽  
Munindra Kumar Singh
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Learning Classifier ◽  
Ensemble Techniques ◽  
Hybrid Machine

Using Polar Expression Features and Nonlinear Machine Learning Classifier for Automated Parkinson’s Disease Screening

2020 ◽  
Vol 20 (1) ◽  
pp. 501-514 ◽  
Author(s):  
Tsung-Lung Yang ◽  
Ping-Ju Kan ◽  
Chia-Hung Lin ◽  
Hsin-Yu Lin ◽  
Wei-Ling Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Screening ◽  
Learning Classifier

scholarly journals Longitudinal clustering analysis and prediction of Parkinson’s disease progression using radiomics and hybrid machine learning

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Mohammad R. Salmanpour ◽  
Mojtaba Shamsaei ◽  
Ghasem Hajianfar ◽  
Hamid Soltanian-Zadeh ◽  
Arman Rahmim
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Clustering Analysis ◽  
Hybrid Machine

Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning

2021 ◽  
Vol 129 ◽  
pp. 104142
Author(s):  
Mohammad R. Salmanpour ◽  
Mojtaba Shamsaei ◽  
Abdollah Saberi ◽  
Ghasem Hajianfar ◽  
Hamid Soltanian-Zadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Robust Identification ◽  
Disease Subtypes ◽  
Hybrid Machine

scholarly journals A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 409
Author(s):  
Marios G. Krokidis ◽  
Georgios N. Dimitrakopoulos ◽  
Aristidis G. Vrahatis ◽  
Christos Tzouvelekis ◽  
Dimitrios Drakoulis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Diagnostic System ◽  
Medical Systems ◽  
Body Protein ◽  
Ensemble Techniques ◽  
Current State

Parkinson’s disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alexandre Boutet ◽  
Radhika Madhavan ◽  
Gavin J. B. Elias ◽  
Suresh E. Joel ◽  
Robert Gramer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
A Priori ◽  
Brain Response ◽  
Brain Responses ◽  
Clinical Benefits ◽  
Deep Brain

AbstractCommonly used for Parkinson’s disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming.

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