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A reliable weighted feature selection for auto medical diagnosis
KTH, School of Information and Communication Technology (ICT), Electronics, Integrated devices and circuits.
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2017 (English)In: Proceedings - 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 985-991, article id 8104907Conference paper, Published paper (Refereed)
Abstract [en]

Feature selection is a key step in data analysis. However, most of the existing feature selection techniques are serial and inefficient to be applied to massive data sets. We propose a feature selection method based on a multi-population weighted intelligent genetic algorithm to enhance the reliability of diagnoses in e-Health applications. The proposed approach, called PIGAS, utilizes a weighted intelligent genetic algorithm to select a proper subset of features that leads to a high classification accuracy. In addition, PIGAS takes advantage of multi-population implementation to further enhance accuracy. To evaluate the subsets of the selected features, the KNN classifier is utilized and assessed on UCI Arrhythmia dataset. To guarantee valid results, leave-one-out validation technique is employed. The experimental results show that the proposed approach outperforms other methods in terms of accuracy and efficiency. The results of the 16-class classification problem indicate an increase in the overall accuracy when using the optimal feature subset. Accuracy achieved being 99.70% indicating the potential of the algorithm to be utilized in a practical auto-diagnosis system. This accuracy was obtained using only half of features, as against an accuracy of66.76% using all the features.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 985-991, article id 8104907
Keywords [en]
Data Analysis, E-Health, Feature Selection, K-Nearest Neighbor Classification, Optimization, Parallel Genetic Algorithm
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-222508DOI: 10.1109/INDIN.2017.8104907ISI: 000427453200147Scopus ID: 2-s2.0-85041236070ISBN: 9781538608371 OAI: oai:DiVA.org:kth-222508DiVA, id: diva2:1182035
Conference
15th IEEE International Conference on Industrial Informatics, INDIN 2017, University of Applied Science Emden/LeerEmden, Germany, 24 July 2017 through 26 July 2017
Note

QC 20180212

Available from: 2018-02-12 Created: 2018-02-12 Last updated: 2018-05-04Bibliographically approved

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Ebrahimi, MasoumehTenhunen, Hannu

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf