Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network

Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network

Alcoholic liver diseases cause high incidence of death worldwide. However, computational diagnosis and classification of alcoholic hepatitis have not yet been established. In this study, we used general regression neural network (GRNN) model with a high-performance classification ability to diagnose...

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Main Authors: Naiping Li, Yongfang Jiang, Jin Ma, Bo He, Wei Tang, Mei Li, Qing Huang, Ting Yuan
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2014/524621
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spelling doaj-38eef59c4b4f476db45ee1ad33a8917b2020-11-24T23:50:24ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472014-01-01201410.1155/2014/524621524621Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural NetworkNaiping Li0Yongfang Jiang1Jin Ma2Bo He3Wei Tang4Mei Li5Qing Huang6Ting Yuan7Liver Diseases Research Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, ChinaLiver Diseases Research Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, ChinaLiver Diseases Research Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, ChinaLiver Diseases Research Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, ChinaLiver Diseases Research Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, ChinaLiver Diseases Research Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, ChinaLiver Diseases Research Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, ChinaLiver Diseases Research Center, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, ChinaAlcoholic liver diseases cause high incidence of death worldwide. However, computational diagnosis and classification of alcoholic hepatitis have not yet been established. In this study, we used general regression neural network (GRNN) model with a high-performance classification ability to diagnose and classify alcohol hepatitis. We used tenfold cross-validation to demonstrate the error rate of networks. The results show an accuracy of 80.91% of the back diagnosis in 110 patients and the accuracy of 81.82% of predicting-diagnosis in 11 patients referring to the clinical diagnosis made by a group of experts. This study suggested that using the liver function tests as the input layer variables of GRNN model could accurately diagnose and classify alcoholic liver diseases.http://dx.doi.org/10.1155/2014/524621
collection DOAJ
language English
format Article
sources DOAJ
author Naiping Li
Yongfang Jiang
Jin Ma
Bo He
Wei Tang
Mei Li
Qing Huang
Ting Yuan
spellingShingle Naiping Li
Yongfang Jiang
Jin Ma
Bo He
Wei Tang
Mei Li
Qing Huang
Ting Yuan
Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network
Mathematical Problems in Engineering
author_facet Naiping Li
Yongfang Jiang
Jin Ma
Bo He
Wei Tang
Mei Li
Qing Huang
Ting Yuan
author_sort Naiping Li
title Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network
title_short Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network
title_full Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network
title_fullStr Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network
title_full_unstemmed Computational Classification and Diagnosis of Alcoholic Liver Diseases Using General Regression Neural Network
title_sort computational classification and diagnosis of alcoholic liver diseases using general regression neural network
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2014-01-01
description Alcoholic liver diseases cause high incidence of death worldwide. However, computational diagnosis and classification of alcoholic hepatitis have not yet been established. In this study, we used general regression neural network (GRNN) model with a high-performance classification ability to diagnose and classify alcohol hepatitis. We used tenfold cross-validation to demonstrate the error rate of networks. The results show an accuracy of 80.91% of the back diagnosis in 110 patients and the accuracy of 81.82% of predicting-diagnosis in 11 patients referring to the clinical diagnosis made by a group of experts. This study suggested that using the liver function tests as the input layer variables of GRNN model could accurately diagnose and classify alcoholic liver diseases.
url http://dx.doi.org/10.1155/2014/524621
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AT yongfangjiang computationalclassificationanddiagnosisofalcoholicliverdiseasesusinggeneralregressionneuralnetwork
AT jinma computationalclassificationanddiagnosisofalcoholicliverdiseasesusinggeneralregressionneuralnetwork
AT bohe computationalclassificationanddiagnosisofalcoholicliverdiseasesusinggeneralregressionneuralnetwork
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