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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
Ma Xiaoyu1,2, Zhang Han1, Zhao Yuhong1,2()
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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Abstract  

[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 words： Back Propagation      Artificial Neural Network      Childhood Asthma      Prediction Model     
Received: 26 February 2018      Published: 08 September 2018
ZTFLH:  R725.6 G35  
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Xiaoyu Ma
Han Zhang
Yuhong Zhao

Cite this article:

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

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0205     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I8/10

数据类型 变量 ACBS中所示名称 类数/数值范围
分类型 孩子性别 RCSGENDR 2
孩子种族 @_RACE 4
医生是否教过吸入器的使用 INHALERH 3
孩子有无保险 INS_TYP 2
家长有无保险 INS1 2
被动吸烟 SMOKE 2
家长受教育程度 @_EDUCAG 3
家庭收入 @_INCOMG 6
家长有无心脏病(或心肌梗塞) CVDINFR4 2
家长有无肾病 CHCKIDNY 2
家长有无糖尿病 DIABETE3 2
家长有无慢阻肺、慢性支气管炎 CHCCOPD1 2
家长有无关节炎 HAVARTH3 2
家长有无抑郁 ADDEPEV2 2
家长有无哮喘 ASTHMA3 2
数值型 孩子确诊年龄 AGEDX 0-3
孩子出生月份 BRTHMNTH 1-12
孩子出生体重 BIRTHW1 0.7-12
母亲生产时年龄 AGEM 9-65
变量名 B S.E. Exp(B) 95%Exp(B) Sig.
下限 上限
ASTHMA3 -0.814 0.220 0.443 0.288 0.682 0.000
INHALERH -0.263 0.040 0.769 0.711 0.831 0.000
AGEDX 0.159 0.070 1.173 1.022 1.345 0.023
@_INCOMG -0.094 0.042 0.910 0.839 0.988 0.025
方法 准确度 灵敏度 特异度
BP人工神经网络 0.723 0.697 0.680
Logistic回归 0.702 0.712 0.492
决策树 0.691 0.708 0.545
支持向量机 0.696 0.712 0.523
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