Parkinson’s Disease Detection Using Biogeography-Based Optimization

• 作者: Atefeh Aghaei Irvan Masoudi Asl Mahnaz Kazemipoor Shaghayegh Vahdat Shahaboddin Shamshirband Somayeh Hessam Timon Rabczuk
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 医学
• 关键词: Parkinson’s disease(PD) biomedical voice measurements multi-layer PERCEPTRON neural network(MLP) biogeography-based optimization(BBO) medical diagnosis BIO-INSPIRED computation
• 年，卷（期）: 2019,（7）
• 页码范围: 11-26
• 页数: 16页
• 分类号: R74

摘要

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.