Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine

Vasily Sachnev, Hyoung Joong Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper, we propose a Binary Coded Genetic Algorithm combined with Extreme learning machine (BCGA-ELM) for Parkinson Disease classification problem. Proposed method analyses ParkDB data base of 22283 genes' expression information extracted from 22 normal patients and 50 Parkinson Disease patients. Proposed method can sufficiently recognize PD patients among normal persons using gene expression information. Besides, the proposed method can also find subset of genes which may be responsible for Parkinson Disease. Chosen subset of genes causes the maximum generalization performance for PD classification problem. Proposed BCGA-ELM also produces a robust solution. In our experiments we executed BCGA-ELM twice started from randomly generated initial data and found same solution at the end. Detected set of 19 genes was also verified by SVM and PBL-McRBFN. Both methods caused maximum generalization performance.

Original languageEnglish
Title of host publicationIEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
PublisherIEEE Computer Society
ISBN (Print)9781479928439
DOIs
Publication statusPublished - 2014 Jan 1
Event9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 - Singapore, Singapore
Duration: 2014 Apr 212014 Apr 24

Other

Other9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014
CountrySingapore
CitySingapore
Period14/4/2114/4/24

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Sachnev, V., & Kim, H. J. (2014). Parkinson Disease Classification based on binary coded genetic algorithm and Extreme learning machine. In IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings [6827649] IEEE Computer Society. https://doi.org/10.1109/ISSNIP.2014.6827649