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Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network

  • Gang Du
  • Xi Liang
  • Xiaoling OuyangEmail author
  • Chunming Wang
Article
  • 37 Downloads

Abstract

Hypertension and its related complications could be a major threat issue for cardiopathy and stroke. Effective prevention and control can decrease the incidence rate of complications in hypertension. Based on the medical data of 3062 patients with cardiovascular and cerebrovascular diseases from 2017 to 2018 in a grade-A tertiary hospital in Shanghai, the study identified the risk factors of hypertension complications by text mining. On this basis, the K2 algorithm based on the improved particle swarm optimization was proposed to optimize the structure of the Bayesian network (BN) by establishing a multi-population cooperative search mechanism. Then the optimized BN was used to analyze and predict the incidence rate of hypertension complications. Results indicate that the major indicators of accuracy, sensitivity, specificity, and AUC have been improved, and the proposed algorithm is superior to the common data mining algorithms such as the back propagation neural network and the decision tree. Through the proposed model and algorithm, the high-risk factors were identified and the occurrence probability of hypertension complications was predicted, which could provide the personalized health management guidance for hypertensive patients to prevent and control hypertension complications.

Keywords

Hypertension complications Risk prediction Intelligent algorithm optimized Bayesian network Improved particle swarm optimization 

Notes

Acknowledgements

We would like to express our sincere gratitude to the editor and anonymous referees for their insightful and constructive comments. This research has been supported by National Natural Science Foundation of China (Grant Nos. 71772065, 71472065), and Shanghai Pujiang Program (14PJC027).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Gang Du
    • 1
  • Xi Liang
    • 1
  • Xiaoling Ouyang
    • 2
    Email author
  • Chunming Wang
    • 3
  1. 1.School of Business and Administration, Faculty of Economics and ManagementEast China Normal UniversityShanghaiChina
  2. 2.School of Economics, Faculty of Economics and ManagementEast China Normal UniversityShanghaiChina
  3. 3.Renji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiChina

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