Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 9
2726-2731

Research and parallel optimization of Parkinson's disease early diagnosis model based on improved salp swarm algorithm

Ma Chaoa
Tan Xub
a. College of Digital Media, b. College of Software, Shenzhen Institute of Information Technology, Shenzhen Guangdong 518172, China
DOI: 10.19734/j.issn.1001-3695.2020.11.0547

Abstract

Parkinson's disease(PD) is a common chronic neurological disease. Because of its unclear etiology, the early diagnosis of is quite difficult. This article proposed a novel model based on improved optimized kernel extreme learning machine(KELM) for the early diagnosis of PD. This paper used chaos theory and Gauss mutation factor to improve salp swarm algorithm(SSA), and designed the intelligent diagnosis model named as ISSA-KELM based on evolution mechanism. The improved SSA(ISSA) used to conduct adjust KELM important parameters and feature subsets selection synchronously. Moreover, the model was processing on multi-thread scheduling on openMP platform, which could achieve the maximum classification accuracy of model, and further improved the computational efficiency. The experimental results show that the classification accuracy of the proposed model is higher than those of the existing methods, the computational efficiency is algo greatly enhanced, it demonstrated the good comprehensive performance. Therefore, it proves that the proposed model has goods prospects for application and it will be helpful for clinicians to make more accurate decision in diagnosis of PD.

Foundation Support

广东省教育厅重点平台及科研项目特色创新类项目(2017GWTSCX040)
2020年度广东省普通高校特色创新项目(KJ2021C006)
深圳市2020年度规划课题(SK2020C018)
2020年校企协同创新项目(SZIIT2021KJ031)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.11.0547
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 9
Section: Algorithm Research & Explore
Pages: 2726-2731
Serial Number: 1001-3695(2021)09-030-2726-06

Publish History

[2021-09-05] Printed Article

Cite This Article

马超, 谭旭. 基于樽海鞘算法优化的帕金森病早期诊断模型研究与并行优化 [J]. 计算机应用研究, 2021, 38 (9): 2726-2731. (Ma Chao, Tan Xu. Research and parallel optimization of Parkinson's disease early diagnosis model based on improved salp swarm algorithm [J]. Application Research of Computers, 2021, 38 (9): 2726-2731. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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