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AI for Health-Related Data Modeling: DCN Application Analysis

AI for Health-Related Data Modeling: DCN Application Analysis

Na Cheng
Copyright: © 2022 |Volume: 13 |Issue: 3 |Pages: 11
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781683181712|DOI: 10.4018/IJISMD.300780
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MLA

Cheng, Na. "AI for Health-Related Data Modeling: DCN Application Analysis." IJISMD vol.13, no.3 2022: pp.1-11. http://doi.org/10.4018/IJISMD.300780

APA

Cheng, N. (2022). AI for Health-Related Data Modeling: DCN Application Analysis. International Journal of Information System Modeling and Design (IJISMD), 13(3), 1-11. http://doi.org/10.4018/IJISMD.300780

Chicago

Cheng, Na. "AI for Health-Related Data Modeling: DCN Application Analysis," International Journal of Information System Modeling and Design (IJISMD) 13, no.3: 1-11. http://doi.org/10.4018/IJISMD.300780

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Abstract

Data modeling of health-related data from Data Center (DC) has positive effects for health monitoring, disease prevention, and healthcare research. However, health-related data has the characteristics of huge, high-dimensional, and non-normalized, which are not beneficial to direct analysis, so data needs to be preprocessed before data modeling. This paper focuses on the features of health-related data, and outlier detection during data preprocessing is studied. Meanwhile, we propose an improved algorithm for health-related data based outlier detection. The experimental results reveal that the proposed outlier detection algorithm has a smaller running time, and more outliers are detected compared to three baselines. In addition, local importance based random forest feature selection algorithm is proposed to measure the importance of each feature. The experimental results indicate that the proposed algorithm can select optimal feature subset to apply health-related data.