Open Access

ARTICLE

Parkinson’s Disease Detection Using Biogeography-Based Optimization

Somayeh Hessam¹, Shaghayegh Vahdat¹, Irvan Masoudi Asl^2,*, Mahnaz Kazemipoor³, Atefeh Aghaei⁴, Shahaboddin Shamshirband,^5,6,*, Timon Rabczuk⁷

1 Department of Health Services Administration, South Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Department of Healthcare Services Management, School of Health Management & Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
3 Clinic for Nutrition and Natural Medicine, Karaj, Iran.
4 Department of Communication, Faculty of Social Sciences, University of Tehran, Tehran, Iran.
5 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
6 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
7 Institute of Structural Mechanics, Bauhaus-University Weimar, Weimar, Germany.

* Corresponding Authors: Irvan Masoudi Asl. Email: ;
  Shahaboddin Shamshirband. Email: .

Computers, Materials & Continua 2019, 61(1), 11-26. https://doi.org/10.32604/cmc.2019.06472

Abstract

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.

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Citations