Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (8): 10-15    DOI: 10.11925/infotech.2096-3467.2018.0205
Current Issue | Archive | Adv Search |
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
Download: PDF(425 KB)   HTML ( 2
Export: BibTeX | EndNote (RIS)      
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 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.

URL:

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

[1] Valet R S, Gebretsadik T, Carroll K N, et al.High Asthma Prevalence and Increased Morbidity Among Rural Children in a Medicaid Cohort[J]. Annals of Allergy Asthma & Immunology, 2011, 106(6): 467-473.
[2] GINA Global Strategy for Asthma Management and Prevention updated 2017[R/OL]. [2017-12-25]. .
[3] Caudri D, Wijga A, Schipper C M A, et al. Predicting the Long-Term Prognosis of Children with Symptoms Suggestive of Asthma at Preschool Age[J]. The Journal of Allergy Clinical Immunology, 2009, 124(5): 903-910.
[4] Smit H A, Pinart M, Antó J M, et al.Childhood Asthma Prediction Models: A Systematic Review[J]. Lancet Respiratory Medicine, 2015, 3(12): 973-984.
[5] Behavioral Risk Factor Surveillance System [DB/OL]. [2017-11-28]..
[6] Castro-Rodríguez J A, Holberg C J, Wright A L, et al. A Clinical Index to Define Risk of Asthma in Young Children with Recurrent Wheezing[J]. American Journal of Respiratory and Critical Care Medicine, 2000, 162(4): 1403-1406.
[7] Brand P L.The Asthma Predictive Index: Not a Useful Tool in Clinical Practice[J]. The Journal of Allergy Clinical Immunology, 2011, 127(1): 293-294.
[8] Van Der Mark L B, Van Wonderen K E, Mohrs J, et al. Predicting Asthma in Preschool Children at High Risk Presenting in Primary Care: Development of a Clinical Asthma Prediction Score[J]. Primary Care Respiratory Journal, 2014, 23(1): 52-59.
[9] Chatzimichail E, Paraskakis E, Rigas A.An Evolutionary Two-Objective Genetic Algorithm for Asthma Prediction[C]// Proceedings of 2013 UKSim 15th International Conference on Computer Modelling and Simulation, Cambridge, United Kingdom. US: IEEE, 2013.
[10] 刘苗苗, 王达, 任万辉, 等. 家养皮毛宠物与儿童哮喘关系[J]. 中国公共卫生, 2012, 28(11): 1420-1430.
[10] (Liu Miaomiao, Wang Da, Ren Wanhui, et al.Relationship Between Pet Keeping and Childhood Asthma in Shenyang City[J]. Chinese Journal of Public Health, 2012, 28(11): 1420-1430.)
[11] 周燕凤. 支气管哮喘发病危险因素分析及相关护理对策[J]. 当代医学, 2011, 17(27): 133-134.
[11] (Zhou Yanfeng.Risk Factors of Bronchial Asthma and Related Nursing Strategies[J]. Contemporary Medicine, 2011, 17(27): 133-134.)
[12] Finkelstein J, Jeong I C.Machine Learning Approaches to Personalize Early Prediction of Asthma Exacerbations[J]. Annals of the New York Academy of Sciences, 2017, 1387(1): 153-165.
[13] Kim W, Kim K S, Lee J E, et al.Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine[J]. Journal of Breast Cancer, 2012, 15(2): 230-238.
[14] 牟冬梅, 任珂. 三种数据挖掘算法在电子病历知识发现中的比较[J]. 现代图书情报技术, 2016(6): 102-109.
[14] (Mu Dongmei, Ren Ke.Discovering Knowledge from Electronic Medical Records with Three Data Mining Algorithms[J]. New Technology of Library and Information Service, 2016(6): 102-109.)
[15] 李丽霞, 张敏, 郜艳晖, 等. 人工神经网络在医学研究中的应用[J]. 数理医药学杂志, 2009, 22(1): 80-82.
[15] (Li Lixia, Zhang Min, Gao Yanhui, et al.Application of Artificial Neural Network in Medical Research[J]. Journal of Mathematical Medicine, 2009, 22(1): 80-82.)
[16] Bisgaard H, Szefler S.Prevalence of Asthma-Like Symptoms in Young Children[J]. Pediatric Pulmonology, 2007, 42(8): 723-728.
[17] Savenije O E, Granell R, Caudri D, et al.Comparison of Childhood Wheezing Phenotypes in 2 Birth Cohorts: ALSPAC and PIAMA[J]. The Journal of Allergy Clinical Immunology, 2011, 127(6): 1505-1512.
[18] Caudri D, Wijga A, Maarten C, et al.Predicting the Long-Term Prognosis of Children with Symptoms Suggestive of Asthma at Preschool Age[J]. The Journal of Allergy Clinical Immunology, 2009, 124(5): 903-910.
[19] 袁梅宇. 数据挖掘与机器学习—WEKA应用技术与实践[M]. 北京: 清华大学出版社, 2014.
[19] (Yuan Meiyu.Data Mining and Machine Learning — WEKA Application Technology and Practice[M]. Beijing: Tsinghua University Press, 2014.)
[20] Kumar Y, Sahoo G.Prediction of Different Types of Liver Diseases Using Rule Based Classification Model[J]. Technology & Health Care, 2013, 21(5): 417-432.
[21] 方积乾. 现代医学统计学[M]. 北京: 人民卫生出版社, 2002: 708-718.
[21] (Fang Jiqian.Advanced Medical Statistics[M]. Beijing: People’s Medical Publishing House, 2002: 708-718.)
[22] Rumelhart D E, Hinton G E, Williams G J, et al.Learning Internal Representation by Back-Propagation Errors[J]. Nature, 1986, 323: 533-536.
[23] Dayhoff J E, Deleo J M.Artificial Neural Networks[J]. Cancer, 2001, 91(8): 1615-1634.
[24] Cross S S, Harrison R F, Kennedy R L.Introduction to Neural Networks[J]. Lancet, 1995, 346(8982): 1075-1079.
[25] 张良均, 王路, 谭立云, 等. Python数据分析与挖掘实战[M]. 北京: 机械工业出版社, 2015.
[25] (Zhang Liangjun, Wang Lu, Tan Liyun, et al.Python Practice of Data Analysis and Mining[M]. Beijing: China Machine Press, 2015.)
[26] 孙振球. 医学统计学[M]. 北京: 人民卫生出版社, 2007: 333-341.
[26] (Sun Zhenqiu.Medical Statistics[M]. Beijing: People’s Medical Publishing House, 2007: 333-341.)
[27] 张良均, 曹晶, 蔡世忠. 神经网络实用教程[M]. 北京: 机械工业出版社, 2008:31-36.
[27] (Zhang Liangjun, Cao Jing, Cai Shizhong.Neural Network Practical Guide[M]. Beijing: China Machine Press, 2008: 31-36.)
[1] Mingqing Zhao,Shengqiang Wu. Research on Stock Market Weighted Prediction Method Based on Micro-blog Sentiment Analysis[J]. 数据分析与知识发现, 2019, 3(2): 43-51.
[2] Mu Dongmei,Ren Ke. Discovering Knowledge from Electronic Medical Records with Three Data Mining Algorithms[J]. 现代图书情报技术, 2016, 32(6): 102-109.
[3] Liu Xisong,Yu Dengke,Li Yue. Construction of Knowledge-based Intelligent Marketing System[J]. 现代图书情报技术, 2008, 24(5): 56-60.
[4] Liang Li,Zhang Yang,Huang Yaming . Rebuilding Index System for Evaluating Internet Resources by Artificial Neural Networks[J]. 现代图书情报技术, 2006, 1(5): 54-57.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn