Dataset Information

Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model.

ABSTRACT:

Background

The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors.

Methodology/principal findings

We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected.

Conclusion/significance

Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (?15) in higher-risk areas was the main target to be intervened for the disease transmission control.

SUBMITTER: Xu JF

PROVIDER: S-EPMC3605232 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model.

Xu Jun-Fang JF Xu Jing J Li Shi-Zhu SZ Jia Tia-Wu TW Huang Xi-Bao XB Zhang Hua-Ming HM Chen Mei M Yang Guo-Jing GJ Gao Shu-Jing SJ Wang Qing-Yun QY Zhou Xiao-Nong XN

PLoS neglected tropical diseases 20130321 3

<h4>Background</h4>The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors.<h4>Methodology/principal findings</h4>We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data ...[more]

PMID: 23556015

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Dataset Information

Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model.

Background

Methodology/principal findings

Conclusion/significance

Publications

Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model.

Similar Datasets

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