Home > Archives > IJSRST173135
Machine Learning Approaches for Diabetes Risk Factor Detection
Authors(2) :-Tejal Anil Patil, Swati A. Patil
Diabetes is a deficiency in the body's ability to convert glucose (sugar) to energy. Glucose is the main source of fuel for our body. When food is digested it is changed into fats, protein, or carbohydrates. Foods that affect blood sugars are called carbohydrates. The hypertriglyceridemic waist (HW) is strongly associated with type 2 diabetes Phenotype; however, to date, no study has assessed the predictive power of phenotypes based on individual triglyceride and anthropometric measurements. The aims of the study were to assess the association between the HW phenotype and type 2 diabetes in Korean adults and to evaluate the predictive power of dissimilar phenotypes consisting of combinations of individual anthropometric measurements and Triglyceride levels. Study measured fasting plasma glucose and TG levels and performed anthropometric measurements. We employed binary logistic regression (LR) to examine statistically significant differences between normal subjects and those with type 2 diabetes using Hypertriglyceridemic waist and individual anthropometric measurements. For more reliable prediction results, two machine learning algorithms, naive Bayes and LR, were used to evaluate the predictive power of various phenotypes.
Tejal Anil Patil, Swati A. Patil
Data Mining, Antropometric measurements, Phenotype, type 2 Diabetes.
- T. Katzmarzyk, C. L. Craig, and L. Gauvin, “Adiposity, physical fitness and incident diabetes: The physical activity longitudinal study,” Diabetologia, vol. 50, no. 3, pp. 538–544, Mar. 2007.
- Xu, X. Qi, A. K. Dahl, and W. Xu, “Waist-to-height ratio is the best indicator for undiagnosed type 2 diabetes,” Diabetic Med., vol. 30, no. 6, pp. e201–e207, Jun. 2013. R. N. Feng, C. Zhao, C. Wang, Y. C. Niu, K. Li, F. C. Guo, S. T. Li, C. H. Sun, and Y. Li, “BMI is strongly associated with hypertension, and waist circumference is strongly associated with type 2 diabetes and dyslipidemia, in northern Chinese adults,” J. Epidemiol., vol. 22, no. 4, pp. 317–323, May 2012.
- Berber, R. G´omez-Santos, G. Fangh¨anel, and L. S´anchez-Reyes, “Anthropometric indexes in the prediction of type 2 diabetes mellitus, hypertension and dyslipidaemia in a Mexican population,” Int. J. Obes. Relat Metab. Disorders, vol. 25, no. 12, pp. 1794–1799, Dec. 2001.
- Balkau, D. Sapinho, A. Petrella, L. Mhamdi, M. Cailleau, D. Arondel, and M. A. Charles, D. E. S. I. R. Study Group, “Prescreening tools for diabetes and obesity-associated dyslipidaemia: Comparing BMI, waist and waist hip ratio. The D.E.S.I.R. Study,” Eur. J. Clin. Nutr., vol. 60, no. 3, pp. 295–304, Mar. 2006.
- S. Okosun, K. M. Chandra, S. Choi, J. Christman, G. E. Dever, and T. E. Prewitt, “Hypertension and type 2 diabetes comorbidity in adults in the United States: risk of overall and regional adiposity,” Obes. Res., vol. 9, no. 1, pp. 1–9, Jan. 2001.
- A. Sargeant, F. I. Bennett, T. E. Forrester, R. S. Cooper, and R. J.Wilks, “Predicting incident diabetes in Jamaica: the role of anthropometry,” Obes. Res., vol. 10, no. 8, pp. 792–798, Aug. 2002.
- T. Duc Son le, T. T. Hanh, K. Kusama, D. Kunii, T. Sakai, N. T. Hung, and S. Yamamoto, “Anthropometric characteristics, dietary patterns and risk of type 2 diabetes mellitus in Vietnam,” J. Amer. Coll. Nutr., vol. 24, no. 4, pp. 229–234, Aug. 2005.
- T. Ko, J. C. Chan, C. S. Cockram, and J.Woo, “Prediction of hypertension, diabetes, dyslipidaemia or albuminuria using simple anthropometric indexes in Hong Kong Chinese,” Int. J. Obes. Relat. Metab. Disorders, vol. 23, no. 11, pp. 1136–1142, Nov. 1999.
- M. B. Snijder, P. Z. Zimmet, M. Visser, J. M. Dekker, J. C. Seidell, and J. E. Shaw, “Independent and opposite associations of waist and hip circumferences with diabetes, hypertension and dyslipidemia: The AusDiab study,” Int. J. Obes. Relat. Metab. Disorders, vol. 28, no. 3, pp. 402–409, Mar. 2004.
Published in : Volume 3 | Issue 1 | January-February 2017
Date of Publication : 2017-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 166-172
Manuscript Number : IJSRST173135
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Tejal Anil Patil, Swati A. Patil, "Machine Learning Approaches for Diabetes Risk Factor Detection", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 1, pp.166-172, January-February-2017
URL : http://ijsrst.com/IJSRST173135