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Cited by 87Artificial Intelligence in HealthcareParkinson's Disease Mechanisms and TreatmentsVoice and Speech Disorders

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A novel ensemble of random forest for assisting diagnosis of Parkinson's disease on small handwritten dynamics dataset

Xu, Shoujiang, Pan, Zhigeng·2020·International Journal of Medical Informatics
DOI 10.1016/j.ijmedinf.2020.104283
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Abstract

Background Parkinson's disease (PD) is a neurodegenerative disease of the elderly, which leads to patients' motor and non-motor disabilities and affects patients' quality of daily life. Timely and effective detection of PD is a key step to medical intervention. Recently, computer aided methods for PD detection have gained lots of attention in artificial intelligence domain. Methods This paper proposed a novel ensemble learning model fusing Random Forest (RF) classifiers and Principal Component Analysis (PCA) technique to differentiate PD patients from healthy controls (HC). Six different RF models were separately constructed to generate the corresponding class probability vectors which represent an individual's category predictions on 6 different handwritten exams, and the final prediction result for an individual was obtained through voting strategy of all RF models. Stratified k-fold cross validation was performed to split the exam datasets and evaluate the classification performances. Results Experimental results prove that our proposed ensemble model on six handwritten exams has achieved better classification performances than a single RF based method on a single handwritten exam. Our ensemble of RF model based on multiple handwritten exams has promising accuracy (89.4 %), specificity (93.7 %), sensitivity (84.5 %) and F1-score (87.7 %). Compared with Logistic Regression (LR) and Support Vector Machines (SVM), the ensemble model based on RF can achieve better classification results. Conclusion A computer-assisted PD diagnosis model on small handwritten dynamics dataset is proposed, and it provides a potential way for assisting diagnosis of PD in clinical setting.

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Metadata

Authors
Xu, S., & Pan, Z.
Affiliations
Hangzhou Normal University (CN)
Corresponding author
Pan, Zhigeng
ORCID
Venue
International Journal of Medical Informatics
Publisher
Elsevier BV
Published
2020-12
Volume
144
Pages
104283
ISSN
1386-5056 (ISSN-L)
DOI
10.1016/j.ijmedinf.2020.104283
OpenAlex
W3088476598
Cited by
87
References
3
Citations by year
2026: 11 · 2025: 18 · 2024: 16 · 2023: 19 · 2022: 18 · 2021: 5
FWCI
9.23
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • International Journal of Medical Informatics (publishedVersion)
  • PubMed (publishedVersion)
Funders
Ministry of Science and Technology of the People's Republic of China
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Clinical Neurology › Parkinson's Disease Mechanisms and Treatments
  • Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
Keywords
Parkinson's disease, computer-aided diagnosis, random forest, principal component analysis, voting classifier
MeSH terms
Aged, Artificial Intelligence, Diagnosis, Computer-Assisted, Humans, Parkinson Disease, Neurodegenerative Diseases, Support Vector Machine

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