Abstract
Obesity is a complex disease arising from an excessive accumulation of body fat which leads to various complications such as diabetes, hypertension, and renal diseases. The growing prevalence of obesity is also becoming a major risk factor for nephropathy. When patients are diagnosed with nephropathy, their progression towards renal failure is usually inevitable. Therefore, a prediction tool will help medical doctors identify patients with a higher risk of developing nephropathy and implement early treatment or prevention. In this study, we attempted to construct a diagnostic support system for nephropathy using clinical and genetic traits. Our results show that prediction models involving the use of both genetic and clinical features yielded the best classification performance. Our finding is in accordance with the complex nature of obesity-related nephropathy and support the notion of using genetic traits to design a personalized diagnostic model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ji, C.Y., Cheng, T.O.: Epidemic increase in overweight and obesity in Chinese children from 1985 to 2005. Int. J. Cardiol. 132, 1–10 (2009)
Iseki, K., Tokashiki, K., Iseki, C., Kohagura, K., Kinjo, K., Takishita, S.: Proteinuria and decreased body mass index as a significant risk factor in developing end-stage renal disease. Clin. Exp. Nephrol. 12, 363–369 (2008)
Kramer, H., Luke, A., Bidani, A., Cao, G., Cooper, R., McGee, D.: Obesity and prevalent and incident CKD: the hypertension detection and follow-up program. Am. J. Kidney Dis. Official J. Nat. Kidney Found. 46, 587–594 (2005)
Maric, C., Hall, J.E.: Obesity, metabolic syndrome and diabetic nephropathy. Contrib. Nephrol. 170, 28–35 (2011)
Sharifiaghdas, F., Kashi, A.H., Eshratkhah, R.: Evaluating percutaneous nephrolithotomy-induced kidney damage by measuring urinary concentrations of β2-microglobulin. Urol. J. 8, 277–282 (2011)
Belle, A., Kon, M.A., Najarian, K.: Biomedical informatics for computer-aided decision support systems: a survey. Sci. World J. 2013, 769639 (2013)
Thorsby, P.M., Midthjell, K., Gjerlaugsen, N., Holmen, J., Hanssen, K.F., Birkeland, K.I., Berg, J.P.: Comparison of genetic risk in three candidate genes (TCF7L2, PPARG, KCNJ11) with traditional risk factors for type 2 diabetes in a population-based study–the HUNT study. Scand. J. Clin. Lab. Invest. 69, 282–287 (2009)
Huang, G.M., Huang, K.Y., Lee, T.Y., Weng, J.: An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients. BMC Bioinform. 16, S5 (2015)
Lin, E., Pei, D., Huang, Y.J., Hsieh, C.H., Wu, L.S.: Gene-gene interactions among genetic variants from obesity candidate genes for nonobese and obese populations in type 2 diabetes. Genet. Test Mol. Biomarkers 13, 485–493 (2009)
Wu, L.S., Hsieh, C.H., Pei, D., Hung, Y.J., Kuo, S.W., Lin, E.: Association and interaction analyses of genetic variants in ADIPOQ, ENPP1, GHSR, PPARgamma and TCF7L2 genes for diabetic nephropathy in a Taiwanese population with type 2 diabetes. Nephrol. Dial. Transplant. 24, 3360–3366 (2009)
Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines (2001)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Akay, M.F.: Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 36, 3240–3247 (2009)
Firouzi, F., Rashidi, M., Hashemi, S., Kangavari, M., Bahari, A., Daryani, N.E., Emam, M.M., Naderi, N., Shalmani, H.M., Farnood, A., Zali, M.: A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software. Eur. J. Gastroenterol. Hepatol. 19, 1075–1081 (2007)
Quinlan, J.R.: Improved use of continuous attributes in C4.5. J. Artif. Intell. Res. 4, 77–90 (1996)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Chen, J., Huang, H.: Feature selection for text classification with Naïve Bayes. Expert Syst. Appl. 36, 5432–5435 (2009)
Guh, R.-S., Wu, T.-C.J., Weng, S.-P.: Integrating genetic algorithm and decision tree learning for assistance in predicting in vitro fertilization outcomes. Expert Syst. Appl. 38, 4437–4449 (2011)
Breiman, L.: Random Forests (2001)
Acknowledgements
Julia Tzu-Ya Weng are supported by the Ministry of Science and Technology of the Republic of China, Taiwan, under the Contract Number of MOST 103-2221-E-155-038, while Yi-Cheng Chen is supported with funding from MOST 104-2221-E-032-037-MY2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, GM., Chen, YC., Weng, J.TY. (2015). Construction of a Prediction Model for Nephropathy Among Obese Patients Using Genetic and Clinical Features. In: Li, XL., Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D. (eds) Trends and Applications in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science(), vol 9441. Springer, Cham. https://doi.org/10.1007/978-3-319-25660-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-25660-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25659-7
Online ISBN: 978-3-319-25660-3
eBook Packages: Computer ScienceComputer Science (R0)