Analyzing performance of classifiers for medical datasets

  • Authors

    • Rosaida Rosly
    • Mokhairi Makhtar
    • Mohd Khalid Awang
    • Mohd Isa Awang
    • Mohd Nordin Abdul Rahman
    2018-04-06
    https://doi.org/10.14419/ijet.v7i2.15.11370
  • Classification model, Data mining, Medical dataset.

  • Abstract

    This paper analyses the performance of classification models using single classification and combination of ensemble method, which are Breast Cancer Wisconsin and Hepatitis data sets as training datasets. This paper presents a comparison of different classifiers based on a 10-fold cross validation using a data mining tool. In this experiment, various classifiers are implemented including three popular ensemble methods which are boosting, bagging and stacking for the combination. The result shows that for the classification of the Breast Cancer Wisconsin data set, the single classification of Naïve Bayes (NB) and a combination of bagging+NB algorithm displayed the highest accuracy at the same percentage (97.51%) compared to other combinations of ensemble classifiers. For the classification of the Hepatitisdata set, the result showed that the combination of stacking+Multi-Layer Perception (MLP) algorithm achieved a higher accuracy at 86.25%. By using the ensemble classifiers, the result may be improved. In future, a multi-classifier approach will be proposed by introducing a fusion at the classification level between these classifiers to obtain classification with higher accuracies.

     

     

  • References

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  • How to Cite

    Rosly, R., Makhtar, M., Khalid Awang, M., Isa Awang, M., & Nordin Abdul Rahman, M. (2018). Analyzing performance of classifiers for medical datasets. International Journal of Engineering & Technology, 7(2.15), 136-138. https://doi.org/10.14419/ijet.v7i2.15.11370

    Received date: 2018-04-10

    Accepted date: 2018-04-10

    Published date: 2018-04-06