Predicting hyperlipidemia using enhanced ensemble classifier

  • Authors

    • Lakshmi K S Rajagiri School of Engineering & Technology
    • G Vadivu Cochin University of Science & Technology
    • Suja Subramanian SRM University
    2018-06-23
    https://doi.org/10.14419/ijet.v7i3.10693
  • Classification, Decision Tree, Hyperlipidemia, Ensemble classifier, Naïve Bayes, Support Vector Machine.

  • Abstract

    Advancement in medical technology has resulted in bulk creation of electronic medical health records.  These health records contain valuable data which are not fully utilized. Efficient usage of data mining techniques helps in discovering potentially relevant facts from medical records. Classification plays an important role in disease prediction. In this paper we developed a prediction model for predicting hyperlipidemia based on ensemble classification. Support Vector Machine, Naïve Bayes Classifier, KNN Classifier and Decision Tree method are combined for developing the ensemble classifier. Performance of each classifier is evaluated separately. An overall accuracy of 97.07% has been obtained by using ensemble approach which is better than the performance of each classifier.

     

     

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  • How to Cite

    K S, L., Vadivu, G., & Subramanian, S. (2018). Predicting hyperlipidemia using enhanced ensemble classifier. International Journal of Engineering & Technology, 7(3), 1114-1118. https://doi.org/10.14419/ijet.v7i3.10693

    Received date: 2018-03-27

    Accepted date: 2018-06-01

    Published date: 2018-06-23