Predicting gastrointestinal infection morbidity based on environmental pollutants: Deep learning versus traditional models |
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Affiliation: | 1. University of Wisconsin-Superior, Superior, WI, United States;2. University of São Paulo, São Paulo, Brazil;3. University of Patras, Patras, Greece;1. IVL Swedish Environmental Research Institute, SE-100 31 Stockholm, Sweden;2. VITO NV Flemish Institute for Technological Research, Boeretang 200, 2400 Mol, Belgium;3. Toxicological Center, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitplein 1, B-2610 Wilrijk, Belgium;4. Department of Environmental Science and Analytical Chemistry (ACES), Stockholm University, SE-106 91 Stockholm, Sweden |
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Abstract: | Accurate morbidity prediction can contribute greatly to the efficiency of medical services. Gastrointestinal infectious diseases are largely influenced by environmental pollutants, but predicting their morbidity based on pollution indicators is quite difficult because of the complex relationship between the pollutants and the infections. This study presents a deep neural network (DNN) model for estimating the morbidity of gastrointestinal infections based on 129 types of pollutants contained in soil and water. The DNN uses a deep Boltzmann machine (DBM) to model the unknown probabilistic relationship between the pollutants, and employs a Gaussian mixture model (GMM) to output the estimated morbidity. We also propose an evolutionary algorithm for efficiently training the DNN. Experiment on a data set from four counties in central China shows that the proposed model can estimate the morbidity much more accurately than traditional neural network and linear regression models. |
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Keywords: | Environmental pollutants Gastrointestinal infections Deep neural network (DNN) Prediction |
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