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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (8): 10-15    DOI: 10.11925/infotech.2096-3467.2018.0205
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Building Childhood Asthma Prediction Model with Artificial Neural Network and BRFSS Database
Xiaoyu Ma1,2,Han Zhang1,Yuhong Zhao1,2()
1Department of Medical Informatics, China Medical University, Shenyang 110122, China
2Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
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[Objective] This study tries to identify high-correlated variables with significant impacts on childhood asthma, aiming to establish predictive model without invasive clinical indicators. [Methods] First, we used statistical methods to identify the needed variables from the BRFSS database. Second, we employed the back propagation artificial neural network to build the prediction model. Finally, we compared the performance of the new model with three other methods: the traditional logistic regression, decision tree and support vector machine. [Results] The identified variables included history of asthma, correct use of inhaler, age of diagnosis, and family income. The proposed model has an accuracy of 0.723, a sensitivity of 0.697 and a specificity of 0.680. [Limitations] The BRFSS database has lots of missing data, which may influence the prediction accuracy. [Conclusions] The self-adaptable BP artificial neural network, could help us establish better prediction models for childhood asthma.

Key wordsBack Propagation      Artificial Neural Network      Childhood Asthma      Prediction Model     
Received: 26 February 2018      Published: 08 September 2018

Cite this article:

Xiaoyu Ma,Han Zhang,Yuhong Zhao. Building Childhood Asthma Prediction Model with Artificial Neural Network and BRFSS Database. Data Analysis and Knowledge Discovery, 2018, 2(8): 10-15.

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