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Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods

Morbidity prediction can be useful in improving the effectiveness and efficiency of medical services, but accurate morbidity prediction is often difficult because of the complex relationships between diseases and their influencing factors. This study investigates the effects of food contamination on...

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Detalles Bibliográficos
Autores principales: Song, Qin, Zheng, Yu-Jun, Yang, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427740/
https://www.ncbi.nlm.nih.gov/pubmed/30866562
http://dx.doi.org/10.3390/ijerph16050838
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author Song, Qin
Zheng, Yu-Jun
Yang, Jun
author_facet Song, Qin
Zheng, Yu-Jun
Yang, Jun
author_sort Song, Qin
collection PubMed
description Morbidity prediction can be useful in improving the effectiveness and efficiency of medical services, but accurate morbidity prediction is often difficult because of the complex relationships between diseases and their influencing factors. This study investigates the effects of food contamination on gastrointestinal-disease morbidities using eight different machine-learning models, including multiple linear regression, a shallow neural network, and three deep neural networks and their improved versions trained by an evolutionary algorithm. Experiments on the datasets from ten cities/counties in central China demonstrate that deep neural networks achieve significantly higher accuracy than classical linear-regression and shallow neural-network models, and the deep denoising autoencoder model with evolutionary learning exhibits the best prediction performance. The results also indicate that the prediction accuracies on acute gastrointestinal diseases are generally higher than those on other diseases, but the models are difficult to predict the morbidities of gastrointestinal tumors. This study demonstrates that evolutionary deep-learning models can be utilized to accurately predict the morbidities of most gastrointestinal diseases from food contamination, and this approach can be extended for the morbidity prediction of many other diseases.
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spelling pubmed-64277402019-04-10 Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods Song, Qin Zheng, Yu-Jun Yang, Jun Int J Environ Res Public Health Article Morbidity prediction can be useful in improving the effectiveness and efficiency of medical services, but accurate morbidity prediction is often difficult because of the complex relationships between diseases and their influencing factors. This study investigates the effects of food contamination on gastrointestinal-disease morbidities using eight different machine-learning models, including multiple linear regression, a shallow neural network, and three deep neural networks and their improved versions trained by an evolutionary algorithm. Experiments on the datasets from ten cities/counties in central China demonstrate that deep neural networks achieve significantly higher accuracy than classical linear-regression and shallow neural-network models, and the deep denoising autoencoder model with evolutionary learning exhibits the best prediction performance. The results also indicate that the prediction accuracies on acute gastrointestinal diseases are generally higher than those on other diseases, but the models are difficult to predict the morbidities of gastrointestinal tumors. This study demonstrates that evolutionary deep-learning models can be utilized to accurately predict the morbidities of most gastrointestinal diseases from food contamination, and this approach can be extended for the morbidity prediction of many other diseases. MDPI 2019-03-07 2019-03 /pmc/articles/PMC6427740/ /pubmed/30866562 http://dx.doi.org/10.3390/ijerph16050838 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Song, Qin
Zheng, Yu-Jun
Yang, Jun
Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods
title Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods
title_full Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods
title_fullStr Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods
title_full_unstemmed Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods
title_short Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods
title_sort effects of food contamination on gastrointestinal morbidity: comparison of different machine-learning methods
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427740/
https://www.ncbi.nlm.nih.gov/pubmed/30866562
http://dx.doi.org/10.3390/ijerph16050838
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