Abstract
Cardiovascular diseases (CVD) are the leading cause of death in the world. Based on density-based spatial clustering of applications with noise algorithm (DBSCAN), we proposed a weight learning approach to utilize the density information of the patient data. The proposed approach divided the sample points of dataset into three types with different weight of density, so that machine learning models achieved better performance in early diagnosis of CVD. Cross-validation on UCI dataset shown that the traditional machine learning models after weight learning can improve accuracy more than 10%.
J. Xie, R. Wu and H. Wang—These authors contributed equally to this work.
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References
Dimmeler, S., Zeiher, A.M.: Circulating microRNAs: novel biomarkers for cardiovascular diseases? Eur. Heart J. 90(8), 865–875 (2012)
Eeg-Olofsson, K., Cederholm, J., et al.: New aspects of HbA1c as a risk factor for cardiovascular diseases in type 2 diabetes: an observational study from the Swedish National Diabetes Register (NDR). J. Intern. Med. 268(5), 471–482 (2010)
Nordestgaard, B.G., Varbo, A.: Triglycerides and cardiovascular disease. Lancet 384(9943), 626–635 (2014)
Members, W.G., Mozaffarian, D., et al.: Heart disease and stroke statistics-2016 update: a report from the american heart association. Circulation 133(4), e38 (2016)
Eleni, R., Adam, T., et al.: Blood pressure and incidence of twelve cardiovascular diseases: lifetime risks, healthy life-years lost, and age-specific associations in 1.25 million people. Lancet 383(9932), 1899–1911 (2014)
Couturier, O., Delalin, H., et al.: A three-step approach for stulong database analysis: characterization of patients groups. In: Proceeding of the ECML/PKDD (2004)
Rao, V.S., Kumar, M.N.: Novel approaches for predicting risk factors of atherosclerosis. IEEE J. Biomed. Health Inform. 17(1), 183–189 (2013)
Hedeshi, N.G., Abadeh, M.S.: Coronary artery disease detection using a fuzzy-boosting PSO Approach. Comput. Intell. Neurosci. 2014, 1–12 (2014)
Kumar, P.R., Priya, M.: Classification of atherosclerotic and non-atherosclerotic individuals using multiclass state vector machine. Technol. Health Care 22(4), 583–595 (2014)
Nikan, S., Gwadry-Sridhar, F., et al.: Machine learning application to predict the risk of coronary artery atherosclerosis. In: International Conference on Computational Science and Computational Intelligence, pp. 34–39 (2017)
Ester, M., Kriegel, H.P., et al.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Kumar, K.M., Reddy, A.R.M.: A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. Pattern Recogn. 58(3), 39–48 (2016)
Schubert, E., Sander, J., et al.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. 42(3), 1–21 (2017)
Debnath, M., Tripathi, P.K., et al.: K-DBSCAN: identifying spatial clusters with differing density levels. In: International Workshop on Data Mining with Industrial Applications, pp. 51–60 (2016)
Dudik, J.M., Kurosu, A., et al.: A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals. Comput. Biol. Med. 59(8), 10–18 (2015)
Hron, K., Templ, M., et al.: Imputation of missing values for compositional data using classical and robust methods. Comput. Stat. Data Anal. 54(12), 3095–3107 (2011)
Kang, P.: Locally linear reconstruction based missing value imputation for supervised learning. Neurocomputing 118(11), 65–78 (2013)
Souto, D., Marcilio, C.P., et al.: Impact of missing data imputation methods on gene expression clustering and classification. BMC Bioinform. 16(1), 1–9 (2015)
Zhu, X., Zhang, S., et al.: Missing value estimation for mixed-attribute data sets. IEEE Trans. Knowl. Data Eng. 23(1), 110–121 (2010)
Eskelson, B.N.I., Temesgen, H., et al.: The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases. Scand. J. For. Res. 24(3), 235–246 (2009)
Jiang, X., Haitao, W., et al.: A novel hybrid subset-learning method for predicting risk factors of atherosclerosis. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 2124–2131 (2017)
Cai, D., Chiyuan, Z., et al.: Unsupervised feature selection for multi-cluster data. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2010)
Pal, M., Foody, G.M.: Feature selection for classification of hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sens. 48(5), 2297–2307 (2010)
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)
Klein, J.P., Wu, J.T.: Discretizing a continuous covariate in survival studies. Handb. Stat. 23(03), 27–42 (2003)
Scott, D.W.: Sturges’ rule. Wiley Interdisc. Rev. Comput. Stat. 1(3), 303–306 (2010)
Scikit-learn Machine Learning in Python. https://scikit-learn.org/stable/
Vehtari, A., Gelman, A., et al.: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27(5), 1413–1432 (2017)
Confusion matrix. https://en.wikipedia.org/wiki/Confusion_matrix
Dua, D., Graff, C.: UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. University of California, School of Information and Computer Science (2017)
Setiawan, N.A., Venkatachalam, P.A., et al.: Rule selection for coronary artery disease diagnosis based on rough set. Int. J. Recent Trends Eng. 2(5), 198–202 (2009)
Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. In: IEEE/ACS International Conference on Computer Systems and Applications (2008)
Alizadehsani, R., Habibi, J., et al.: A data mining approach for diagnosis of coronary artery disease. Comput. Methods Program. Biomed. 111(1), 52–61 (2013)
Rajeswari, K., Vaithiyanathan, V., et al.: Feature selection for classification in medical data mining. Int. J. Emerg. Trends Technol. Comput. Sci. 2(2), 492–497 (2013)
Acknowledgements
This research was supported by the National Key R&D Program of China [No. 2017YFB0701501], the National Natural Science Foundation of China [No. 61873156] and the Project of NSFS [No. 17ZR1409900].
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Xie, J., Wu, R., Wang, H., Kong, Y., Li, H., Zhang, W. (2019). A Novel Weight Learning Approach Based on Density for Accurate Prediction of Atherosclerosis. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_18
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