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Mutius, E. V. (2016). The microbial environment and its influence on asthma prevention in early life. The Journal of Allergy and Clinical Immunology, 137(3): 680-689.

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Article

Two-Stage Artificial Neural Network Regression Modelling for Wheezing Risk Factors Among Children - A Case Study of Gatundu Hospital, Kenya

1Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Kenya


American Journal of Applied Mathematics and Statistics. 2021, Vol. 9 No. 2, 38-47
DOI: 10.12691/ajams-9-2-1
Copyright © 2021 Science and Education Publishing

Cite this paper:
Thomas Mageto, John Onyango. Two-Stage Artificial Neural Network Regression Modelling for Wheezing Risk Factors Among Children - A Case Study of Gatundu Hospital, Kenya. American Journal of Applied Mathematics and Statistics. 2021; 9(2):38-47. doi: 10.12691/ajams-9-2-1.

Correspondence to: Thomas  Mageto, Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Kenya. Email: tttmageto@gmail.com

Abstract

In Kenya wheezing that leads to asthma development in most cases remain under-diagnosed and under-treated. Currently there is no public supported wheezing and asthma care programmes to optimize care for patients with asthma which greatly compounds diagnosis and treatment of the disease. The aim of this study is therefore to consider and analyse the covariates of childhood wheezing among children below 10 years of age in Kenya, a case study of Gatundu hospital in order to improve the provision of wheezing and asthma care services in medical facilities. The possible risk factors in the study are selected from three major groups of demographic, socioeconomic and geographical location factors related to childhood wheezing. The longitudinal secondary data obtained from Gatundu hospital in Kenya were collected and a total of 584 complete cases were recorded. The predictor variables considered in the study include age of children in months, gender, exclusive breastfeeding, exposure to tobacco smoking, difficult living conditions, residence, atopy, maternal age and preterm births. Due to the binary nature of response variable in which data is recorded as presence or absence of wheezing, the risk factors were modelled using multiple logistic regression and Artificial Neural Network Models. Simple random samples of sizes n = 385 without replacement were selected and p-values at 5% level of significance for the variables were recorded. In multiple logistic regression, the five variables identified as possible risk factors for modelling with p-value less than or equal to 0.05 were selected that includes age of children, exclusive breastfeeding, exposure to tobacco smoking, difficult living conditions and residence that recorded p-values of 0.0151, 0.0000, 0.0071, 0.0274 and 0.0410. The best multiple logistic linear regression model selected was based on Akaike Information Criterion (AIC) criterion that recorded null deviance, residual deviance and AIC of 502.44, 179.57 and 191.57 respectively. The precision and accuracy of the multiple logistic regression model were recorded as 89.2% and 93.3% respectively. The Artificial Neural Network was considered for modelling as well, the model with one-hidden layer with four neurons in the hidden layer recorded precision of 97.1% and accuracy of 39.4% while the rest of the models with one hidden layer recorded precision and accuracy of 0.0% and 65.1% respectively. The Artificial Neural Network model with two-hidden layers were also considered and the Network with one neuron in both layers was selected as better performing model with precision and accuracy of 88.2% and 93.3%. The developed two-stage logistic Artificial Neural Network was found to have better performance compared to multiple linear logistic regression and Artificial Neural Networks since it recorded precision and accuracy of 97.1% and 99.0% respectively and hence recommended for consideration in modelling the risk factor of wheezing among children in Kenya.

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