Abstract
Due to the problems in health examination records such as irregular examinations, missing values in examination indicators, and various time span, it is difficult to give a holistic view on personal or population health situations. In this paper, we propose a tensor decomposition method to deal with the missing data problem in health examination records by using a big data set collected over seven years from a medium size Chinese city. According to TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and entropy weighting method, the weights of indicators reflecting the health status are calculated to establish a health index for residential health examination. In the forms of grade diagram and fingerprint diagram, the changes of the individual health index as well as corresponding ranking position and development trend are visualized. In our experiments, we demonstrate the useful-ness and the effectiveness of our approach.
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Lai, G., Yu, D., Zhang, S., Wei, Z., Sun, X. (2020). Personal Health Index Based on Residential Health Examination. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_43
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DOI: https://doi.org/10.1007/978-3-030-65390-3_43
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