Abstract
Recently, various studies have shown that meaningful knowledge can be discovered by applying data mining techniques in medical applications, i.e., decision support systems for disease diagnosis. However, there are still several computational challenges due to the high-dimensionality of medical data. Feature selection is an essential pre-processing procedure in data mining to identify relevant feature subset for classification. In this study, we proposed a hybrid feature selection mechanism by combining symmetrical uncertainty and Bayesian network. As a case study, we applied our proposed method to the hypertension diagnosis problem. The results showed that our method can improve the classification performance and outperformed existing feature selection techniques.
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References
Korea National Statistical Office: Annual report on the statistical for elderly (2011)
Black, D.S., O’Reilly, G.A., Olmstead, R., Breen, E.C., Irwin, M.R.: Mindfulness meditation and improvement in sleep quality and daytime impairment among older adults with sleep disturbances: a randomized clinical trial. JAMA Intern. Med. 175(4), 494–501 (2015)
Jeong, H.S., Song, Y.M.: Contributing factors to the increases in health insurance expenditures for the aged and their forecasts. Korean J. Health Econ. Policy 19(2), 21–38 (2013)
Korea Centers for Disease Control and Prevention: Korea National health & nutrition examination survey (2007–2014)
Bae, Y.H., Shin, J.S., Lee, J., Kim, M.R., Park, K.B., Cho, J.H., Ha, I.H.: Association between Hypertension and the prevalence of low back pain and osteoarthritis in Koreans: a cross-sectional study. PloS one, 10(9) (2015)
Song, S., Paik, H.Y., Song, W.O., Song, Y.: Metabolic syndrome risk factors are associated with white rice intake in Korean adolescent girls and boys. Br. J. Nutr. 113(03), 479–487 (2015)
Ha, I.H., Lee, J., Kim, M.R., Kim, H., Shin, J.S.: The association between the history of cardiovascular diseases and chronic low back pain in South Koreans: a cross-sectional study. PloS one 9(4) (2014)
Piao, Y., Piao, M., Park, K., Ryu, K.H.: An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data. Bioinformatics 28(24), 3306–3315 (2012)
Piao, Y., Piao, M., Ryu, K.H.: Multiclass cancer classification using a feature subset-based ensemble from microRNA expression profiles. Comput. Biol. Med. 80, 39–44 (2017)
Lee, D.G., Ryu, K.S., Bashir, M., Bae, J.W., Ryu, K.H.: Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction. J. Med. Syst. 37(2), 9896 (2013)
Bashir, M.E.A., Shon, H.S., Lee, D.G., Kim, H., Ryu, K.H.: Real-time automated cardiac health monitoring by combination of active learning and adaptive feature selection. TIIS 7(1), 99–118 (2013)
Kim, H., Ishag, M.I.M., Piao, M., Kwon, T., Ryu, K.H.: A data mining approach for cardiovascular disease diagnosis using heart rate variability and images of carotid arteries. Symmetry 8(6), 4 (2016)
Mayer, C., Bachler, M., Holzinger, A., Stein, P.K., Wassertheurer, S.: The effect of threshold values and weighting factors on the association between entropy measures and mortality after myocardial infarction in the Cardiac Arrhythmia Suppression Trial (CAST). Entropy 18(4), 129, 121–115 (2016)
Kaur, H., Wasan, S.K.: Empirical study on applications of data mining techniques in healthcare. J. Comput. Sci. 2(2), 194–200 (2006)
Holzinger, A.: Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop? Brain Inform. 3(2), 119–131 (2016)
Hund, M., Boehm, D., Sturm, W., Sedlmair, M., Schreck, T., Ullrich, T., Keim, D.A., Majnaric, L., Holzinger, A.: Visual analytics for concept exploration in subspaces of patient groups: Making sense of complex datasets with the Doctor-in-the-loop. Brain Inform. 3(4), 233–247 (2016)
Park, H.W., Batbaatar, E., Li, D., Ryu, K.H.: Risk factors rule mining in hypertension: Korean National Health and Nutrient Examinations Survey 2007–2014. In: Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1–4 (2016)
Yang, Y., Liao, Y., Meng, G., Lee, J.: A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis. Expert Syst. Appl. 38(9), 11311–11320 (2011)
Hsu, H.H., Hsieh, C.W., Lu, M.D.: Hybrid feature selection by combining filters and wrappers. Expert Syst. Appl. 38(7), 8144–8150 (2011)
Xie, J., Wang, C.: Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 38, 5809–5815 (2011)
Vapnik, V.: The nature of statistical learning (2013)
Han, J., Fu, Y.: Attribute-oriented induction in data mining. Advances in knowledge discovery and data mining, pp. 399–421 (1996)
Acknowledgment
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2013-0-00881) supervised by the IITP (Institute for Information & communication Technology Promotion) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4010826).
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Park, H.W., Li, D., Piao, Y., Ryu, K.H. (2017). A Hybrid Feature Selection Method to Classification and Its Application in Hypertension Diagnosis. In: Bursa, M., Holzinger, A., Renda, M., Khuri, S. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2017. Lecture Notes in Computer Science(), vol 10443. Springer, Cham. https://doi.org/10.1007/978-3-319-64265-9_2
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DOI: https://doi.org/10.1007/978-3-319-64265-9_2
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