2018
DOI: 10.14419/ijet.v7i2.15.11370
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Analyzing performance of classifiers for medical datasets

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 c… Show more

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Cited by 7 publications
(7 citation statements)
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“…To demonstrate the superiority and robustness of our proposed model, we conducted experiments on an independent testing set. Huynh et al [ 29 ] proposed an ensemble method that obtained 83.42% accuracy and 0.8418 AUC on this dataset, and Rosly et al [ 30 ] proposed a stacking technique combined with a multilayer perceptron that obtained 86.25% accuracy. In comparison, as shown in Table 6 , our IHCP has improved in both AUC and accuracy and is more suitable as a prediction model for hepatitis C.…”
Section: Resultsmentioning
confidence: 99%
“…To demonstrate the superiority and robustness of our proposed model, we conducted experiments on an independent testing set. Huynh et al [ 29 ] proposed an ensemble method that obtained 83.42% accuracy and 0.8418 AUC on this dataset, and Rosly et al [ 30 ] proposed a stacking technique combined with a multilayer perceptron that obtained 86.25% accuracy. In comparison, as shown in Table 6 , our IHCP has improved in both AUC and accuracy and is more suitable as a prediction model for hepatitis C.…”
Section: Resultsmentioning
confidence: 99%
“…By improving the attribute selection process and handling missing data more effectively, J48 aims to provide more accurate and robust predictions. The resulting decision trees can offer insights into the key factors influencing the co-disease prediction [25].…”
Section: J48 (C45 or C50)mentioning
confidence: 99%
“…The performance of ensemble models in medical diagnosis has not been extensively studied, with the exception of [27] and [15], which show that bagging and boosting learning perform better than a single model. The fact that the medical dataset is typically class-imbalanced is yet another factor that can make things more complicated.…”
Section: Related Workmentioning
confidence: 99%