Abstract
Fatty liver often afflicts patients seriously and jeopardizes the health of human race with high possibility of deteriorating into cirrhosis and liver cancer, which motivates researchers to detect causes and potential influential factors. In this paper, we study the problem of detecting the potential influential factors in workplaces and their contributions to the morbidity. To this end, gender and age, retirement status and department information are chosen as three potential influential factors in workplaces. By analyzing those factors with demographics, Propensity Score Matching and classic classifier models, we mine the relationship between the workplace factors and morbidity. This finding indicates a new domain of discussing the causes of fatty liver which originally focuses on daily diets and lifestyles.
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Wei, H. et al. (2014). Predicting Health Care Risk with Big Data Drawn from Clinical Physiological Parameters. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_8
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DOI: https://doi.org/10.1007/978-3-662-45558-6_8
Publisher Name: Springer, Berlin, Heidelberg
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