ArticleScientific reports2016
Accuracy Improvement for Predicting Parkinson's Disease Progression.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Application of Unsupervised Clustering Methods to Alzheimer's Disease.Frontiers in computational neuroscience · 2019Pooled it
- Trial
- Vocal Feature Changes for Monitoring Parkinson's Disease Progression-A Systematic Review.Brain sciences · 2025Review
- 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
- Article
- Article
- Hybrid Machine Learning Framework for Multistage Parkinson's Disease Classification Using Acoustic Features of Sustained Korean Vowels.Bioengineering (Basel, Switzerland) · 2023Article
- Parkinson's Disease Diagnosis Using Laplacian Score, Gaussian Process Regression and Self-Organizing Maps.Brain sciences · 2023Article
- Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification.Sensors (Basel, Switzerland) · 2023Article
- Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.Scientific reports · 2022Article
- Parkinson's disease severity clustering based on tapping activity on mobile device.Scientific reports · 2022Article
- Predicting Parkinson's Disease Progression: Evaluation of Ensemble Methods in Machine Learning.Journal of healthcare engineering · 2022Article
- Article
- An Efficient Rotation Forest-Based Ensemble Approach for Predicting Severity of Parkinson's Disease.Journal of healthcare engineering · 2022Article
- A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia.Frontiers in neuroscience · 2022Article
- Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations.Brain sciences · 2021Article
- Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson's disease using machine learning.Scientific reports · 2021Article
- Data-Driven Models for Objective Grading Improvement of Parkinson's Disease.Annals of biomedical engineering · 2020Article
- Alterations of functional connectivities associated with autism spectrum disorder symptom severity: a multi-site study using multivariate pattern analysis.Scientific reports · 2020Article
- Artificial Neural Network-Based Prediction of Outcome in Parkinson's Disease Patients Using DaTscan SPECT Imaging Features.Molecular imaging and biology · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson's datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.