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

被引:64
作者
Nilashi, Mehrbakhsh [1 ]
Ahmadi, Hossein [2 ,3 ]
Shahmoradi, Leila [4 ]
Ibrahim, Othman [1 ]
Akbari, Elnaz [5 ,6 ]
机构
[1] Univ Teknol Malaysia, Fac Engn, Sch Comp, Utm Johor Bahru 81310, Johor, Malaysia
[2] Iran Univ Med Sci, Sch Hlth Management & Informat Sci, Dept Hlth Informat Management, Tehran, Iran
[3] FDA, Hatal Res Ctr IRI, Tehran, Iran
[4] Univ Tehran Med Sci, Sch Allied Med Sci, Hlth Informat Management Dept, Tehran, Iran
[5] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam
[6] Ton Duc Thang Univ, Fac Elect & Elect Engn, Ho Chi Minh City, Vietnam
关键词
Viral hepatitis; Public health; NIPALS; SOM; ANFIS ensemble; Decision trees; NATIONAL-GUARD PERSONNEL; B-VIRUS; MACHINE; SYSTEM; EPIDEMIOLOGY; INFECTION; KNOWLEDGE; HISTORY; CANCER;
D O I
10.1016/j.jiph.2018.09.009
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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. (c) 2018 The Authors. Published by Elsevier Limited on behalf of King Saud Bin Abdulaziz University for Health Sciences.
引用
收藏
页码:13 / 20
页数:8
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