Enhanced decision tree induction using evolutionary techniques for Parkinson’s disease classification

• Autorzy: Ghane Mostafa , Ang Mei Choo , Nilashi Mehrbakhsh , Sorooshian Shahryar

Identyfikatory

DOI

10.1016/j.bbe.2022.07.002

• Języki publikacji: EN

Abstrakty

EN

The diagnosis of Parkinson’s disease (PD) is important in neurological pathology for appropriate medical therapy. Algorithms based on decision tree induction (DTI) have been widely used for diagnosing PD through biomedical voice disorders. However, DTI for PD diagnosis is based on a greedy search algorithm which causes overfitting and inferior solutions. This paper improved the performance of DTI using evolutionary-based genetic algorithms. The goal was to combine evolutionary techniques, namely, a genetic algorithm (GA) and genetic programming (GP), with a decision tree algorithm (J48) to improve the classification performance. The developed model was applied to a real biomedical dataset for the diagnosis of PD. The results showed that the accuracy of the J48, was improved from 80.51% to 89.23% and to 90.76% using the GA and GP, respectively.

Słowa kluczowe

EN

decision tree induction; decision tree algorithm; J48; genetic algorithm; genetic programming; Parkinson’s disease

PL

drzewo decyzyjne; algorytm drzewa decyzyjnego; J48; algorytm genetyczny; programowanie genetyczne; choroba Parkinsona

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 3
• Strony: 902--920
• Opis fizyczny: Bibliogr. 123 poz., rys., tab., wykr.

Twórcy

autor

Ghane Mostafa

• Institute of IR4.0, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia

autor

Ang Mei Choo

• Institute of IR4.0, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia

autor

Nilashi Mehrbakhsh

• UCSI Graduate Business School, UCSI University, Kuala Lumpur, Malaysia
• Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, Penang, Malaysia

autor

Sorooshian Shahryar

• Department of Business Administration, University of Gothenburg, Gothenburg, Sweden

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