Abstract
Large volumes of data are being generated each day in healthcare. In addition, these huge amounts of data from healthcare datasets cause the issue of proper knowledge discovery. Currently, data integration is an approach, which is increasingly utilized by healthcare data specialists for analyzing the information and data mining. “Which features or attributes should we use to integrate data for data warehouses”-is a difficult question to answer. It requires deep knowledge of the problem domain. Automatic feature selection is the process of selecting a subset of relevant features automatically for later use. In this paper, we proposed a method using four random forest based feature selection algorithm and domain knowledge. Experimental results show that our hybrid method can select a required number of features from a large set of attributes.
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Badiuzzaman Biplob, M., Khan, S.I., Sheraji, G.A., Shuvo, J.A. (2020). Hybrid Feature Selection Algorithm to Support Health Data Warehousing. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_10
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DOI: https://doi.org/10.1007/978-3-030-38501-9_10
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