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A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets

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Book cover Computational Intelligence in Optimization

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 7))

Abstract

In this article we have studied the usage of a classification method based on preprocessing the data first using principal component analysis, and then using the compressed data in actual classification process which is based on differential evolution algorithm, an evolutionary optimization algorithm. This method is applied here for prediction diagnosis from clinical data sets with chief complaint of chest pain using classical Electronic Medical Record (EMR), heart data sets. For experimentation we used a set of five frequently applied benchmark data sets including Cleveland, Hungarian, Long Beach, Switzerland and Statlog data sets. These data sets are containing demographic properties, clinical symptoms, clinical findings, laboratory test results specific electrocardiography (ECG), results pertaining to angina and coronary infarction, etc. In other words, classical EMR data pertaining to the evaluation of a chest pain patient and ruling out angina and/or Coronary Artery Disease, (CAD). The prediction diagnosis results with the proposed classification approach were found promisingly accurate. For example, the Switzerland data set was classified with 94.5 % ±0.4 % accuracy. Combining all these data sets resulted in the classification accuracy of 82.0 % ±0.5 %. We compared the results of the proposed method with the corresponding results of the other methods reported in the literature that have demonstrated relatively high classification performance in solving this problem. Depending on the case, the results of the proposed method were of equal level with the best compared methods, or outperformed their classification accuracy clearly. In general, the results are suggesting that the proposed method has potential in this task.

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Author information

Authors and Affiliations

  1. Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FIN-53851, Lappeenranta, Finland

    Pasi Luukka

  2. Department of Computer Science, University of Vaasa, P.O. Box 700, FI-65101, Vaasa, Finland

    Jouni Lampinen

Authors
  1. Pasi Luukka
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  2. Jouni Lampinen
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Editors and Affiliations

  1. Department of Mechanical Engineering and Science-Faculty of Engineering, Kyoto University, Yoshida-honmachi,Sakyo-Ku, 606-8501, Kyoto, Japan

    Yoel Tenne

  2. Advanced Technology Centre, Rolls-Royce Singapore Pte Ltd, 50 Nanyang Avenue, Block N2,Level B3C,Unit 05-08, 639798, Singapore

    Chi-Keong Goh

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Luukka, P., Lampinen, J. (2010). A Classification method based on principal component analysis and differential evolution algorithm applied for prediction diagnosis from clinical EMR heart data sets. In: Tenne, Y., Goh, CK. (eds) Computational Intelligence in Optimization. Adaptation, Learning, and Optimization, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12775-5_11

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  • DOI: https://doi.org/10.1007/978-3-642-12775-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12774-8

  • Online ISBN: 978-3-642-12775-5

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