Feature Selection and Machine Learning Methods for Optimal Identification and Prediction of Subtypes in Parkinson's Disease

2021 ◽  
pp. 106131
Author(s):  
Mohammad R. Salmanpour ◽  
Mojtaba Shamsaei ◽  
Arman Rahmim
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Learning Methods ◽  
Machine Learning Methods

Machine learning methods for optimal prediction of motor outcome in Parkinson’s disease

2020 ◽  
Vol 69 ◽  
pp. 233-240 ◽  
Author(s):  
Mohammad R. Salmanpour ◽  
Mojtaba Shamsaei ◽  
Abdollah Saberi ◽  
Ivan S. Klyuzhin ◽  
Jing Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Optimal Prediction ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Motor Outcome

scholarly journals Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 778
Author(s):  
Vasco Ponciano ◽  
Ivan Miguel Pires ◽  
Fernando Reinaldo Ribeiro ◽  
Gonçalo Marques ◽  
Maria Vanessa Villasana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensors ◽  
Automatic Recognition ◽  
Learning Methods ◽  
Less Invasive ◽  
Machine Learning Methods ◽  
The Future

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease

2019 ◽  
Vol 111 ◽  
pp. 103347 ◽  
Author(s):  
Mohammad R. Salmanpour ◽  
Mojtaba Shamsaei ◽  
Abdollah Saberi ◽  
Saeed Setayeshi ◽  
Ivan S. Klyuzhin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Outcome ◽  
Learning Methods ◽  
Machine Learning Methods
Sign in / Sign up

Export Citation Format

Share Document