Paper Info
Title
Automatic Classification And Monitoring Of Denovo Parkinson'S Disease By Learning Demographic And Clinical Features
Abstract
Parkinson's Disease (PD) is the second most prevalent progressive neurological disorder around the world with high incidence rates for seniors. Since most symptoms are exposed in the later stages of the disease, early diagnosis of PD is essential for more effective treatment. The motivation of this research is early automatic assessment of PD using clinical information, not only for disease diagnosis but also for monitoring progression. After preprocessing the data, feature selection is done by the Mean Decrease Impurity (MDI) method. In the classification step, Random Forest (RF) is used as a classifier model for two tasks, including (1) classifying the subjects to PD and Healthy Control (HC), and (2) determining the disease severity level by Hoehn & Yahr (H&Y) scale. The clinical data used is taken from the Parkinson's Progression Markers Initiative (PPMI) database, which is the most prominent source of data for PD. Experimental results show promising performance of the proposed model for assessment of PD by incorporating clinical properties.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857729
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Parkinson's disease,Disease,Incidence (epidemiology),Healthy control,Feature selection,Computer science,Neurological disorder,Artificial intelligence,Physical medicine and rehabilitation,Random forest
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Sara Soltaninejad102.03
Anup Basu274997.26
Irene Cheng328335.18