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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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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