ArticleJournal of healthcare engineering2022
Predicting Parkinson's Disease Progression: Evaluation of Ensemble Methods in Machine Learning.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Enhancing prediction accuracy for Parkinson's disease using advanced machine learning models.Scientific reports · 2026Article
- An explainable ensemble machine learning model using baseline blood transcriptomics to predict Parkinson's disease motor progression.Frontiers in digital health · 2026Article
- Non-motor symptoms as critical predictors of quality of life in Parkinson's disease: a machine learning approach.Health and quality of life outcomes · 2025Observational
- Advancements in Parkinson's Disease Prediction Using Machine Learning: A Neurological Perspective.Healthcare informatics research · 2025Article
- Item Response Modeling and Artificial Neural Network for Differentiation of Parkinson's Patients and Subjects Without Evidence of Dopaminergic Deficit.CPT: pharmacometrics & systems pharmacology · 2025Article
- Comprehensive clinical scale-based machine learning model for predicting subthalamic nucleus deep brain stimulation outcomes in Parkinson's disease.Neurosurgical review · 2025Article
- A new ensemble learning method stratified sampling blending optimizes conventional blending and improves prediction performance.Bioinformatics advances · 2025Article
- Deep Learning for Parkinson's Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein-Peptide Datasets.Diagnostics (Basel, Switzerland) · 2024Article
- Predicting Quality of Life in Parkinson's Disease: A Machine Learning Approach Employing Common Clinical Variables.Journal of clinical medicine · 2024Article
- White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.Quantitative imaging in medicine and surgery · 2024Article
- A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches.Diagnostics (Basel, Switzerland) · 2023Article
- Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees.Heliyon · 2023Article
- Parkinson's Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps.Brain sciences · 2023Article
- The Association between Depression Severity, Prosody, and Voice Acoustic Features in Women with Depression.TheScientificWorldJournal · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.
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