preprints.org > computer science and mathematics > computer vision and graphics > doi: 10.20944/preprints201905.0125.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 8 May 2019 / Approved: 10 May 2019 / Online: 10 May 2019 (13:56:59 CEST)

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– reasonable 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.

Parkinson’s disease (PD); Biomedical voice measurements; Multi-layer Perceptron Neural Network (MLP); Biogeography-based Optimization (BBO); Medical diagnosis. Bio-inspired computation

Computer Science and Mathematics, Computer Vision and Graphics

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