Abstract
In recent years, there has been a high prevalence rate of heart disease (HD) among 50-year-old people in China. It has become the first disease of old ages death. It is a very interesting and challenging work to have an effective early forecasting of the risk of HD according to the patients data. In this paper, we propose a novel method to analyze the factors with views of group features. Normalized mutual information based on entropies and information gain ratio are employed to select factors. Discriminant minimum class locality preserving canonical correlation analysis is presented to determine the effectiveness of the view of group factors. Moreover, a novel model is given to forecast the risks of New York Heart Association Functional Classification. To verify the effectiveness of the proposed method and model, we collected electronic health records of 1271 patients from 28 Chinese Level III-A hospitals in 2015. After the risk factors analysis, several results are concluded: (1) Patients with HD usually suffer from similar complications. For example, most patients with heart disease suffer from hypertension, diabetes and arrhythmia at the same time. (2) The risk forecasting has an accurate recognition rate. The risk value of the level of patients is impacted on the complications. (3) Hypertension, arrhythmia, chronic cardiac insufficiency and coronary disease are the highest concurrent diseases. There is a high reliability to have a decision of levels on the cardiac functional diseases according to the output of our proposed model.
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References
Aleix M, Avinash CK (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233
Blokh D, Stambler I (2015) Information theoretical analysis of aging as a risk factor for heart disease. Aging Dis 6(3):196–207
Chen W, Fan X et al (2016) Report on cardiovascular disease in China (2015). Chin Circul J 31(6):1
Chen W, Gao R, Liu L, Zhu M (2015) Report on cardiovascular diseases in China. Chin Circul J 30(7):617–622
Chinese Medical Association: Guidelines for The Diagnosis and Treatment of Heart Failure in China (2014). Chin J Cardiol 42(2):2014
D’Angelo G, Pilla R, Tascini C, Rampone S (2019) A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees. Soft Comput 23:11775–11791
Heidenreich PA, Albert NM et al (2013) Forecasting the impact of heart failure in the United States. NIH Public Access 6(3):606–619
Jabez CJ, Khanna NH et al (2015) A swarm optimization approach for clinical knowledge mining. Comput Methods Programs Biomed 121(3):137–138
Kupper N, Bonhof C, Westerhuis B (2016) Determinants of dyspnea in chronic heart failure. J Cardiac Fail 22(3):201–209
Lei B, Chen S et al (2016) Discriminative learning for Alzheimer’s disease diagnosis via canonical correlation analysis and multimodal fusion. Front Aging Neurosci 8:77
Liu C, Wang W et al (2017) An efficient instance selection algorithm to reconstruct training set for support vector machine. Knowl-Based Syst 116:58–73
Mendis S, Puska P, Norrving B (2011) Global Atlas on cardiovascular disease prevention and control. World Health Organization, Geneva, pp 3–18
Mercer AJ (2016) Long-term trends in cardiovascular disease mortality and association with respiratory disease. Epidemiol Infect 144(4):777–786
Methaila A, Kansal P et al (2014) Early heart disease prediction using data mining techniques. Comput Sci Inf Technol 4(8):53–59
Peng Y, Zhang D, Zhang J (2010) A new canonical correlation analysis algorithm with local discrimination. Neural Process Lett 31(1):1–15
Samuel OW, Asogbon GM et al (2017) An integrated decision support system based on ANN and Fuzzy\_AHP for heart failure risk prediction. Expert Syst Appl 68:163–172
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(4):379C423
Stankovic I, Neskovic AN, Putnikovic B,Apostolovic S, Lainscak M, Edelmann F, Doehner W, Gelbrich G, Inkrot S, Rau T, Herrmann-Lingen C, Anker SD, Dngen HD (2012) Sinus rhythm versus atrial fibrillation in elderly patients with chronic heart failure—Insight from the Cardiac Insufficiency Bisoprolol Study in Elderly. Int J Cardiol 161(3):160–165
Wang S, Jianfeng L et al (2016) Canonical principal angles correlation analysis for two-view data. J Vis Commun Image Represent 35:209–219
Warren-Gash S, Liam H, Andrew C (2009) Influenza as a trigger for acute myocardial infarction or death from cardiovascular disease: a systematic review. Lancet Infect Dis 9(10):601–610
Wen J, Fang X, Cui J, Fei L, Yan K, Chen Y, Xu Y (2019) Robust sparse linear discriminant analysis. IEEE Trans Circuits Syst Video Techonol 29(2):390–403
Xing X, Wang K et al (2016) Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recognit 50:107–117
Yubo Y, Chenglong M, Dongmei P (2016) A novel discriminant minimum class locality preserving canonical correlation analysis and its applications. J Ind Manag Optim 12(1):251–268
Zhang X, Ding S et al (2017) An improved multiple birth support vector machine for pattern classification. Neurocomputing 225:119–128
Zhang H, Wang P et al (2016) Risk factors of heart failure for patients classification with extreme learning machine. In: Proceeding of ICMLC2016 conference, Jeju, South Korea, pp 814–819
Zhao T, Yuan Y, Wang Y, Gao J, He P (2017) Heart disease classification based on feature fusion. In: 2017 International conference on machine learning and cybernetics (ICMLC), Ningbo, pp 111–117
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Luo, J., Yan, H. & Yuan, Y. Risk Factors Analysis and Classification on Heart Disease. Soft Comput 24, 13167–13178 (2020). https://doi.org/10.1007/s00500-020-04731-z
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DOI: https://doi.org/10.1007/s00500-020-04731-z