Research article

Research and prediction of opioid crisis based on BP neural network and Markov chain

  • Received: 16 June 2019 Accepted: 19 August 2019 Published: 05 September 2019
  • MSC : 62P25; 97K80; 97M10

  • Nowadays, in the United States, opioid abuse is so serious that it has become a crisis, causing serious health impacts and huge losses to the US economy. To study the social and economic data and the relationship between opioid drug abuse situation, this paper uses Grey Relation Analysis to analyze the identification and counting of synthetic opioids and heroin provided by NFLIS and the data related to socioeconomic factors provided by the United States Census Bureau. After that, the orderly clustering is used to introduce the corresponding level. Then, BP neural network and Markov model are built to forecast the degree of the flood of opioid. The Proportion of Low Education Level People, Number of New Pregnant Women, The Proportion of The Elderly Living Alone and the other 7 factors were selected as the input nodes of the neural network. Based on the prediction results of the BP neural network, Markov Chain is used to correct the residual sequence. It is found that the deviation of prediction is reduced from[-10.99%, 22.33%] to[-8.29%, 2.81%], making the modified value closer to the measured value. This method combines the advantages of the BP neural network and the Markov network, which improves the accuracy of prediction and provides certain reference values for opioid prediction. Finally, we propose strategies to address the opioid crisis and test their effectiveness.

    Citation: Wanchun Fan, Yan Jiang, Songyang Huang, Weiguo Liu. Research and prediction of opioid crisis based on BP neural network and Markov chain[J]. AIMS Mathematics, 2019, 4(5): 1357-1368. doi: 10.3934/math.2019.5.1357

    Related Papers:

  • Nowadays, in the United States, opioid abuse is so serious that it has become a crisis, causing serious health impacts and huge losses to the US economy. To study the social and economic data and the relationship between opioid drug abuse situation, this paper uses Grey Relation Analysis to analyze the identification and counting of synthetic opioids and heroin provided by NFLIS and the data related to socioeconomic factors provided by the United States Census Bureau. After that, the orderly clustering is used to introduce the corresponding level. Then, BP neural network and Markov model are built to forecast the degree of the flood of opioid. The Proportion of Low Education Level People, Number of New Pregnant Women, The Proportion of The Elderly Living Alone and the other 7 factors were selected as the input nodes of the neural network. Based on the prediction results of the BP neural network, Markov Chain is used to correct the residual sequence. It is found that the deviation of prediction is reduced from[-10.99%, 22.33%] to[-8.29%, 2.81%], making the modified value closer to the measured value. This method combines the advantages of the BP neural network and the Markov network, which improves the accuracy of prediction and provides certain reference values for opioid prediction. Finally, we propose strategies to address the opioid crisis and test their effectiveness.


    加载中


    [1] X. Xianhai, Fisher ordered clustering method and its application in extraction of temperature anomalies in furnace tubes, Guangxi Dianli, (2005), 15-17.
    [2] H. Dan, A review of sociological theories on drug abuse abroad-a case study of relevant research results in the United States, Realism, (2010), 86-89.
    [3] C. S. Davis, A. J. Lieberman, H. Hernandez-Delgado, et al. Laws limiting the prescribing or dispensing of opioids for acute pain in the United States:A national systematic legal review., Drug Alcohol Depen., 194 (2019), 166-172. doi: 10.1016/j.drugalcdep.2018.09.022
    [4] W. Ke, P. Hemin, G. Jiatai, Research on the spread of opioid abuse and its trend prediction, Journal of Technology Wind, (2019), 244.
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3973) PDF downloads(469) Cited by(3)

Article outline

Figures and Tables

Figures(5)  /  Tables(8)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog