ArticleDiagnostics (Basel, Switzerland)2023
Parkinson's Disease Detection Using Filter Feature Selection and a Genetic Algorithm with Ensemble Learning.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 26 citations in OpenAlex.
- Toward Interpretable Voice-Based Parkinson's Disease Screening via Joint Transfer Function-Feature-Classifier-Ensemble Selection.Bioengineering (Basel, Switzerland) · 2026Article
- ShPCFHNet: shepherd parallel convolutional forward harmonic net for spinal cord injury detection using CT images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Enhanced meta ensemble stacking approach with XGBoost and optuna based detection of Parkinson's disease.Frontiers in digital health · 2026Article
- The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder marked by motor and non-motor symptoms that have a severe impact on the quality of life of the affected individuals. This study explores the effect of filter feature selection, followed by ensemble learning methods and genetic selection, on the detection of PD patients from attributes extracted from voice clips from both PD patients and healthy patients. Two distinct datasets were employed in this study. Filter feature selection was carried out by eliminating quasi-constant features. Several classification models were then tested on the filtered data. Decision tree, random forest, and XGBoost classifiers produced remarkable results, especially on Dataset 1, where 100% accuracy was achieved by decision tree and random forest. Ensemble learning methods (voting, stacking, and bagging) were then applied to the best-performing models to see whether the results could be enhanced further. Additionally, genetic selection was applied to the filtered data and evaluated using several classification models for their accuracy and precision. It was found that in most cases, the predictions for PD patients showed more precision than those for healthy individuals. The overall performance was also better on Dataset 1 than on Dataset 2, which had a greater number of features.
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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.