Parkinson’s Disease Detection Using Filter Feature Selection and a Genetic Algorithm with Ensemble Learning

Ali, Abdullah Marish and Salim, Farsana and Saeed, Faisal (2023) Parkinson’s Disease Detection Using Filter Feature Selection and a Genetic Algorithm with Ensemble Learning. Diagnostics, 13 (17). p. 2816. ISSN 2075-4418

diagnostics-13-02816.pdf - Published Version
Available under License Creative Commons Attribution.

Download (989kB)


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.

Item Type: Article
Identification Number:
25 August 2023Accepted
31 August 2023Published Online
Uncontrolled Keywords: Parkinson’s disease (PD), filter feature selection, ensemble learning, genetic selection
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 15 Feb 2024 15:29
Last Modified: 15 Feb 2024 15:29

Actions (login required)

View Item View Item


In this section...