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A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique

Nilashi, Mehrbakhsh and Ahmadi, Hossein and Shahmoradi, Leila and Ibrahim, Othman and Akbari, Elnaz (2019) A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. Journal of Infection and Public Health, 12 (1). pp. 13-20. ISSN 1876-0341


Official URL: http://dx.doi.org/10.1016/j.jiph.2018.09.009


Background: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning. Methods: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies. Results: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine. Conclusions: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.

Item Type:Article
Uncontrolled Keywords:public health, SOM, viral hepatitis
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:89193
Deposited By: Yanti Mohd Shah
Deposited On:22 Feb 2021 14:00
Last Modified:22 Feb 2021 14:00

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