Abstract
Chronics diseases have become the major cause of the death for human all over the world and every country had to pay a heavy price for it in past years. It is of great significance for researchers to find out the risk factors that affect the onset, maintenance and prognosis of a variety of chronic diseases. In this research we aim to find out the risk factor of chronics from questionnaire data in Pizhou city by GRI. We develop some customized preprocessing methods according to the characteristics of questionnaire data and discover some significant results in conformance with medical conclusion. The result shows that obesity, smoking and lack of sleeping are three vital risk factors which could cause some chronic diseases.
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References
Nikolajsen, L., Brandsborg, B., Lucht, U., Jensen, T.S., Kehlet, H.: Chronic pain following total hip arthroplasty: a nationwide questionnaire study. Acta Anaesthesiol Scand. 50, 495–500 (2006)
Talley, N.J., Newman, P., Boyce, P.M., Paterson, K.J., Owen, B.K.: Initial validation of a bowel symptom questionnaire and measurement of chronic gastrointestinal symptoms in Australians. Internal Medicine Journal 25(4), 302–308 (1995)
Masson, L.F., MCNeill, G., Tomany, J.O., Simpson, J.A., Peace, H.S., Wei, L., Grubb, D.A., Bolton-Smith, C.: Statistical approaches for assessing the relative validity of a food-frequency questionnaire: use of correlation coefficients and the kappa statistic. Public Health Nutrition 6, 313–321 (2003)
Hagströmer, M., Oja, P., Sjöström, M.: The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutrition 9, 755–762 (2006)
Brossette, S.E., Sprague, A.P., Jones, W.T., Moser, S.A.: A Data Mining System for Infection Control Surveillance [J]. Methods of Information in Medicine 39, 303–310 (2010)
Li, J., Fu, A.W., He, H., Chen, J.: Mining Risk Patterns in Medical Data. In: International Conference on Knowledge Discovery and Data Mining KDD 2005, pp. 770–775 (2005)
Lia, L., Tanga, H., Wu, Z., Gong, J., Gruidl, M., Zou, J., Tockman, M., Clark, R.A.: Data mining techniques for cancer detection using serum proteomic profiling. Artificial Intelligence in Medicine 32, 71–83 (2004)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Databases, pp. 487–499 (1994)
Clementine 11.1, Clementine Algorithms Guide, Integral solutions Limited (2007)
Smyth, P., Goodman, R.M.: An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering, 310–316 (1992)
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© 2014 Springer International Publishing Switzerland
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Zheng, Z., Li, Y., Cai, Y. (2014). An Analysis on Risk Factors of Chronics Diseases Based on GRI. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_5
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DOI: https://doi.org/10.1007/978-3-319-06269-3_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06268-6
Online ISBN: 978-3-319-06269-3
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