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

Author

Listed:
  • Gang Du

    (East China Normal University)

  • Xi Liang

    (East China Normal University)

  • Xiaoling Ouyang

    (East China Normal University)

  • Chunming Wang

    (Shanghai Jiao Tong University)

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.

Suggested Citation

  • Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 0. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-22.
    • Handle: RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-019-00485-z
    DOI: 10.1007/s10878-019-00485-z
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    1. Branco Ponomariov, 2013. "Government-sponsored university-industry collaboration and the production of nanotechnology patents in US universities," The Journal of Technology Transfer, Springer, vol. 38(6), pages 749-767, December.
    2. Lutz Bornmann & Robin Haunschild & Sven E. Hug, 2018. "Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 427-437, February.
    3. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    4. Martin Meyer, 2007. "What do we know about innovation in nanotechnology? Some propositions about an emerging field between hype and path-dependency," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(3), pages 779-810, March.
    5. Terrill L. Frantz, 2018. "Blockmap: an interactive visualization tool for big-data networks," Computational and Mathematical Organization Theory, Springer, vol. 24(2), pages 149-168, June.
    6. Ying Yang & Shoucheng Luo & Jing Fan & Xinye Zhou & Chunyu Fu & Guochun Tang, 2019. "Study on specialist outpatient matching appointment and the balance matching model," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 20-39, January.
    7. Gang Du & Luyao Zheng & Xiaoling Ouyang, 2019. "Real-time scheduling optimization considering the unexpected events in home health care," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 196-220, January.
    8. Jing Li & Ming Dong & Yijiong Ren & Kaiqi Yin, 2015. "How patient compliance impacts the recommendations for colorectal cancer screening," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 920-937, November.
    9. Wei Gao & Wuping Bao & Xin Zhou, 2019. "Analysis of cough detection index based on decision tree and support vector machine," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 375-384, January.
    10. Yanqin Bai & Xiao Han & Tong Chen & Hua Yu, 2015. "Quadratic kernel-free least squares support vector machine for target diseases classification," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 850-870, November.
    11. Ropero, R.F. & Renooij, S. & van der Gaag, L.C., 2018. "Discretizing environmental data for learning Bayesian-network classifiers," Ecological Modelling, Elsevier, vol. 368(C), pages 391-403.
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