This paper use the neural network model to an epidemiological study, i.e. bovine tuberculosis (bTB) occurrence in cattle herds, together with well-established risk factors in the area known as West Wicklow, in the east of Ireland. The binary target variable is whether the herd is in the restricted status, which is defined by whether any bTB reactor is detected in the herd. To estimate the parameters and prevent over-fitting in neural network model fitting, the observations are divided into three part of Training data set, Validation data set, and Test data set. By analysis on the lift charts on test data set, the fitted neural network model can be used to enhance practice efficiency.
Published in | American Journal of Applied Mathematics (Volume 3, Issue 4) |
DOI | 10.11648/j.ajam.20150304.16 |
Page(s) | 201-205 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
Neural Network Model, Bovine Tuberculosis, Spearman’s Rank Correlation, Lift Chart
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APA Style
Renhao Jin, Fang Yan, Jie Zhu. (2015). Application of Neural Network Model in an Epidemiological Study. American Journal of Applied Mathematics, 3(4), 201-205. https://doi.org/10.11648/j.ajam.20150304.16
ACS Style
Renhao Jin; Fang Yan; Jie Zhu. Application of Neural Network Model in an Epidemiological Study. Am. J. Appl. Math. 2015, 3(4), 201-205. doi: 10.11648/j.ajam.20150304.16
AMA Style
Renhao Jin, Fang Yan, Jie Zhu. Application of Neural Network Model in an Epidemiological Study. Am J Appl Math. 2015;3(4):201-205. doi: 10.11648/j.ajam.20150304.16
@article{10.11648/j.ajam.20150304.16, author = {Renhao Jin and Fang Yan and Jie Zhu}, title = {Application of Neural Network Model in an Epidemiological Study}, journal = {American Journal of Applied Mathematics}, volume = {3}, number = {4}, pages = {201-205}, doi = {10.11648/j.ajam.20150304.16}, url = {https://doi.org/10.11648/j.ajam.20150304.16}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20150304.16}, abstract = {This paper use the neural network model to an epidemiological study, i.e. bovine tuberculosis (bTB) occurrence in cattle herds, together with well-established risk factors in the area known as West Wicklow, in the east of Ireland. The binary target variable is whether the herd is in the restricted status, which is defined by whether any bTB reactor is detected in the herd. To estimate the parameters and prevent over-fitting in neural network model fitting, the observations are divided into three part of Training data set, Validation data set, and Test data set. By analysis on the lift charts on test data set, the fitted neural network model can be used to enhance practice efficiency.}, year = {2015} }
TY - JOUR T1 - Application of Neural Network Model in an Epidemiological Study AU - Renhao Jin AU - Fang Yan AU - Jie Zhu Y1 - 2015/08/01 PY - 2015 N1 - https://doi.org/10.11648/j.ajam.20150304.16 DO - 10.11648/j.ajam.20150304.16 T2 - American Journal of Applied Mathematics JF - American Journal of Applied Mathematics JO - American Journal of Applied Mathematics SP - 201 EP - 205 PB - Science Publishing Group SN - 2330-006X UR - https://doi.org/10.11648/j.ajam.20150304.16 AB - This paper use the neural network model to an epidemiological study, i.e. bovine tuberculosis (bTB) occurrence in cattle herds, together with well-established risk factors in the area known as West Wicklow, in the east of Ireland. The binary target variable is whether the herd is in the restricted status, which is defined by whether any bTB reactor is detected in the herd. To estimate the parameters and prevent over-fitting in neural network model fitting, the observations are divided into three part of Training data set, Validation data set, and Test data set. By analysis on the lift charts on test data set, the fitted neural network model can be used to enhance practice efficiency. VL - 3 IS - 4 ER -