Prediction of Type 2 Diabetes through Risk Factors using Binary Logistic Regression
DOI:
https://doi.org/10.29304/jqcm.2020.12.3.709Keywords:
Type-2 Diabetes, Risk Factors, Logistic Regression, PredictionAbstract
The main objective of this study is to arrive at a highly efficient prediction model for early prediction of diabetes by relying on risk factors for diabetes as predictor variables.
Using a binary logistic regression model, a model was built for the data of study which taken from a sample of diabetics and non-diabetics persons.
The results have shown the high ability of the binary logistic regression model in predicting the diabetes-infected persons. All indicators confirm the validity and quality of the model. The results of Chi-square test (sig=0.945>.05) indicated that the model is significant. Likewise, the results of Hosmer&Lemeshow test (sig=0.945>.05) confirm that the model represents the data very well. Classification table findings were also high, as the overall percentage for the correct classification was 91%.
The significant risk factors that influential in predicting diabetes can be arranged as follows: (High blood pressure, Diabetes in the family first degree, High cholesterol, Smoking, Age above 35, Overweight and Gender).
All these results confirm the quality and accuracy of this model in predicting the disease, the thing which may indicate that the model can be used as a primary tool for predicting type-2 diabetes through risk factors.
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References
[2] Cataloguing, W. L. (2016) ‘Global Report on Diabetes’, Isbn, 978, pp. 6–86. Available at: http://www.who.int/about/licensing/copyright_form/index.html%0Ahttp://www.who.int/about/licensing/copyright_form/index.html%0Ahttps://apps.who.int/iris/handle/10665/204871%0Ahttp://www.who.int/about/licensing/.
[3] Hosmer, D. W. and Lemeshow, S. (2005) Applied Logistic Regression, Applied Logistic Regression. doi: 10.1002/0471722146.
[4] Islam, R. and Rahman, O. (2012) ‘The Risk Factors of Type 2 Diabetic Patients Attending Rajshahi Diabetes Association, Rajshahi, Bangladesh and Its Primary Prevention’, Food and Public Health, 2(2), pp. 5–11. doi: 10.5923/j.fph.20120202.02.
[5] Kleinbaum, D. G. and Klein, M. (2002) Logistic Regression A Self-Learning Text Second Edition, Survival.
[6] Maulana, Y. I. R., Badriyah, T. and Syarif, I. (2018) ‘Influence of Logistic Regression Models For Prediction and Analysis of Diabetes Risk Factors’, EMITTER International Journal of Engineering Technology, 6(1), pp. 151–167. doi: 10.24003/emitter.v6i1.258.
[7] Niyikora, S. (2015) ‘Multiple logistic regression modeling on risk factors of diabetes. Case study of Gitwe Hospital (2011-2013)’. Available at : http: // www.jkuat.ac.ke/ campuses/kigali/wp-content/uploads/2014/04/NiyikoraSylivere2015-Multiple-logistic-regression-modeling-on-risk-factors-of-diabetescase-study-of-Gitwe-hospital-2011-2013.pdf.
[8] Rahimloo, P. and Jafarian, A. (2016) ‘Prediction of Diabetes by Using Artificial Neural Network, Logistic Regression Statistical Model and Combination of Them’, Bulletin de la Société Royale des Sciences de Liège, 85, pp. 1148–1164.
[9] Rahman, A. (2013) ‘Application of Artificial Neural Network and Binary Logistic Regression in Detection of Diabetes Status’, Science Journal of Public Health, 1(1), p. 39. doi: 10.11648/j.sjph.20130101.16.
[10] Rastogi, P. and Singh, B. K. (2019) ‘A multivariate binary logistic regression modeling for a ssessing various risk factors that affect diabetes’, International Journal of Scientific and Technology Research, 8(8), pp. 589–599.
[11] Risk, N. C. D. and Collaboration, F. (2016) ‘Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants’, Lancet (London, England), 387(10027), pp. 1513–1530. doi: 10.1016/S0140-6736(16)00618-8.
[12] Saied, A. and Abdallah, R. (2019) ‘Using logistic regression models to determine factors affecting diabetes in the red sea state’, 4(4), pp. 12–17.
[13] Senthilvel, V., Radhakrishnan, R. and Sathiyamoorthi, R. (2011) ‘Prediction of diabetic retinopathy among diabetics using binary logistic regression approach’, Indian Journal of Medical Specialities, 3(1). doi: 10.7713/ijms.2012.0005.
[14] Sperandei, S. (2014) ‘Understanding logistic regression analysis’, Biochemia Medica, 24(1), pp. 12–18. doi: 10.11613/BM.2014.003.
[15] Zehra, A. et al. (2018) ‘Statistical modeling for prediction of diabetes in Malaysians’, Life Science Journal, 15(6).