Hybrid feature selection framework for the parkinson imbalanced dataset prediction problem

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dc.authorid0000-0003-4511-7694en_US
dc.contributor.authorQasim, Hayder Mohammed
dc.contributor.authorAta, Oğuz
dc.contributor.authorAnsari, Mohammad Azam
dc.contributor.authorAlomary, Mohammad N.
dc.contributor.authorAlghamdi, Saad
dc.contributor.authorAlmehmadi, Mazen
dc.date.accessioned2022-01-14T12:59:11Z
dc.date.available2022-01-14T12:59:11Z
dc.date.issued2021en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractBackground and Objectives: Recently, many studies have focused on the early detection of Parkinson’s disease (PD). This disease belongs to a group of neurological problems that immediately affect brain cells and influence the movement, hearing, and various cognitive functions. Medical data sets are often not equally distributed in their classes and this gives a bias in the classification of patients. We performed a Hybrid feature selection framework that can deal with imbalanced datasets like PD. Use the SOMTE algorithm to deal with unbalanced datasets. Removing the contradiction from the features in the dataset and decrease the processing time by using Recursive Feature Elimination (RFE), and Principle Component Analysis (PCA). Materials and Methods: PD acoustic datasets and the characteristics of control subjects were used to construct classification models such as Bagging, K-nearest neighbour (KNN), multilayer perceptron, and the support vector machine (SVM). In the prepressing stage, the synthetic minority over-sampling technique (SMOTE) with two-feature selection RFE and PCA were used. The PD dataset comprises a large difference between the numbers of the infected and uninfected patients, which causes the classification bias problem. Therefore, SMOTE was used to resolve this problem. Results: For model evaluation, the train–test split technique was used for the experiment. All the models were Grid-search tuned, the evaluation results of the SVM model showed the highest accuracy of 98.2%, and the KNN model exhibited the highest specificity of 99%. Conclusions: the proposed method is compared with the current modern methods of detecting Parkinson’s disease and other methods for medical diseases, it was noted that our developed system could treat data bias and reach a high prediction of PD and this can be beneficial for health organizations to properly prioritize assets.en_US
dc.identifier.citationQasim, H. M., Ata, O., Ansari, M. A., Alomary, M. N., Alghamdi, S., & Almehmadi, M. (2021). Hybrid Feature Selection Framework for the Parkinson Imbalanced Dataset Prediction Problem. Medicina, 57(11), 1217.en_US
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85119620686
dc.identifier.scopusqualityQ2
dc.identifier.startpage1217en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2146
dc.identifier.volume57en_US
dc.identifier.wosWOS:000726690900001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorQasim, Hayder Mohammed
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.publisherMDPIen_US
dc.relation.ispartofMedicina
dc.relation.isversionofDOI: 10.3390/medicina57111217en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectParkinson Detectionen_US
dc.subjectMachine Learningen_US
dc.subjectPCAen_US
dc.subjectRFEen_US
dc.subjectSMOTEen_US
dc.titleHybrid feature selection framework for the parkinson imbalanced dataset prediction problem
dc.typeArticle

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