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.
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References
Antza C, Cifkova R, Kotsis V (2018) Hypertensive complications of pregnancy: a clinical overview. Metabolism 86:102–111
Arlot S, Celisse A (2010) A survey of cross-validation procedures for model selection. Stat Surveys 4:40–79
Bai Y, Han X, Chen T et al (2015) Quadratic kernel-free least squares support vector machine for target diseases classification. J Comb Optim 30(4):850–870
Bornmann L, Haunschild R, Hug SE (2018) Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis. Scientometrics 114(2):427–437
Borsuk ME, Stow CA, Reckhow KH (2004) A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecol Model 173(2–3):219–239
Burgoslunar CD, Salinerofort MA, Cárdenasvalladolid J et al (2011) Validation of diabetes mellitus and hypertension diagnosis in computerized medical records in primary health care. BMC Med Res Methodol 11(1):146
Cooper GF, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309–347
Dagliati A, Marini S, Sacchi L et al (2018) Machine learning methods to predict diabetes complications. J Diabetes Sci Technol 12(2):295–302
Devereux RB, De SG, Ganau A et al (1993) Left ventricular hypertrophy associated with hypertension and its relevance as a risk factor for complications. J Cardiovasc Pharmacol 21(1):S38–44
Ding S, Li Z, Liu X et al (2019) Diabetic complication prediction using a similarity-enhanced latent Dirichlet allocation model. Inf Sci 499:12–24
Du G, Jiang Z, Diao X et al (2012) Knowledge extraction algorithm for variances handling of CP using integrated hybrid genetic double multi-group cooperative PSO and DPSO. J Med Syst 36(2):979–994
Du G, Jiang Z, Diao X et al (2013) Intelligent ensemble T-S fuzzy neural networks with RCDPSO\_DM optimization for effective handling of complex clinical pathway variances. Comput Biol Med 43(6):613–634
Du G, Zheng L, Ouyang X (2019) Real-time scheduling optimization considering the unexpected events in home health care. J Comb Optim 37(1):196–220
Feldman R, Dagan I (1995) Knowledge discovery in textual databases (KDT). KDD 95:112–117
Frantz TL (2018) Blockmap: an interactive visualization tool for big-data networks. Comput Math Organ Theory 33:1–20
Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29(2–3):131–163
Gao W, Bao W, Zhou X (2019) Analysis of cough detection index based on decision tree and support vector machine. J Comb Optim 37(1):375–384
Gheisari S, Meybodi MR, Dehghan M et al (2017) Bayesian network structure training based on a game of learning automata. Int J Mach Learn Cybern 8(4):1093–1105
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36
Kahle D, Savitsky T, Schnelle S (2008) Rice university. STAT 631/ELEC 639: graphical models, junction tree algorithm [EB/OL]. Available at https://www.ece.rice.edu/~vc3/elec633/jta.pdf. Accessed 21 Nov 2019
Kim DH (2002) Bayesian and empirical Bayesian analysis under informative sampling. Sankhyā Indian J Stat Ser B 64(3):267–288
Kitson MT, Roberts SK, Colman JC et al (2015) Liver stiffness and the prediction of clinically significant portal hypertension and portal hypertensive complications. Scand J Gastroenterol 50(4):462–469
Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT press, Cambridge
Kshirsagar AV, Chiu Y, Bomback AS et al (2010) A hypertension risk score for middle-aged and older adults. J Clin Hypertens 12(10):800–808
Kumar CA, Srinivas S (2010) Concept lattice reduction using fuzzy K-means clustering. Expert Syst Appl 37(3):2696–2704
Lee M, Entzminger L (2006) Risk factors of hypertension and correlates of blood pressure and mean arterial pressure among patients receiving health exams at the preventive medicine clinic, King Chulalongkorn Memorial Hospital, Thailand. J Med Assoc Thail 89(8):1213–1221
Lee W, Lee J, Lee H et al (2014) Prediction of hypertension complications risk using classification techniques. Ind Eng Manag Syst 13(4):449–453
Lee J, Lee W, Park IS et al (2016) Risk assessment for hypertension and hypertension complications incidences using a Bayesian network. IIE Trans Healthc Syst Eng 6(4):246–259
Lee J, Kwon RH, Kim HW et al (2018) A data-driven procedure of providing a health promotion program for hypertension prevention. Service Sci 10(3):289–301
Li J, Dong M, Ren Y et al (2015) How patient compliance impacts the recommendations for colorectal cancer screening. J Comb Optim 30(4):920–937
Mensah GA (1999) Refining strategies for the prevention and control of hypertension and related complications. Ethn Disease 9(3):327–332
Meyer M (2007) What do we know about innovation in nanotechnology? Some propositions about an emerging field between hype and path-dependency. Scientometrics 70(3):779–810
Ojha R, Ghadge A, Tiwari MK et al (2018) Bayesian network modelling for supply chain risk propagation. Int J Product Res 56(17):5795–5819
Parikh Nisha I (2008) A risk score for predicting near-term incidence of hypertension: the Framingham heart study. Ann Intern Med 148(2):102
Pearl J (1987) Evidential reasoning using stochastic simulation of causal models. Artif Intell 32(2):245–257
Ponomariov B (2013) Government-sponsored university-industry collaboration and the production of nanotechnology patents in US universities. J Technol Transf 38(6):749–767
Qureshi AI, Suri MFK, Kirmani JF et al (2005) Is prehypertension a risk factor for cardiovascular diseases? Stroke J Cereb Circ 36(9):1859
Rachmani R, Levi Z, Slavachevski I et al (2010) Teaching patients to monitor their risk factors retards the progression of vascular complications in high-risk patients with type 2 diabetes mellitus-a randomized prospective study. Diabet Med 19(5):385–392
Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255
Ropero RF, Renooij S, Van der Gaag LC (2018) Discretizing environmental data for learning Bayesian-network classifiers. Ecol Model 368:391–403
Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol 3. IEEE, pp 1945–1950
Staessen JA, Wang JG, Thijs L (2001) Cardiovascular protection and blood pressure reduction: a meta-analysis. Lancet 358(9290):1305–1315
Tavana M, Abtahi AR, Di Caprio D et al (2018) An artificial neural network and Bayesian network model for liquidity risk assessment in banking. Neurocomputing 275:2525–2554
Vasan RS, Larson MG, Leip EP et al (2001) Impact of high-normal blood pressure on the risk of cardiovascular disease. N Engl J Med 11(2):31–31
Wiysonge CUS, Blackett KN, Mbuagbaw JN (2004) Risk factors and complications of hypertension in Yaounde, Cameroon: cardiovascular topics. Cardiovasc J S Afr 15(5):215–219
Wu Q (2010) Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM. Expert Syst Appl 37(1):194–201
Yang Y, Luo S, Fan J et al (2019) Study on specialist outpatient matching appointment and the balance matching model. J Comb Optim 37(1):20–39
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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Du, G., Liang, X., Ouyang, X. et al. Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network. J Comb Optim 42, 966–987 (2021). https://doi.org/10.1007/s10878-019-00485-z
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DOI: https://doi.org/10.1007/s10878-019-00485-z