Logistic Regression Versus Neural Networks: The Best Accuracy in Prediction of Diabetes Disease
DOI:
https://doi.org/10.51983/ajcst-2017.6.2.1782Keywords:
Artificial Neural Networks (ANNs), logistic regression (LR), Quasi-Newton Algorithm, Resilient - Back propagation, Standard Back PropagationAbstract
To derive actionable insights from vast amount of data in an intelligent fashion some techniques are used called machine learning techniques. These techniques support for predicting disease with correct case of training and testing. To classify the medical data logistic regression and artificial neural networks are the models to be selected. Today in world a major health problem is Diabetes Mellitus for which many classification algorithms have been applied for its diagnoses and treatment. To detect diabetes disease in early stage it needs greatest support of machine learning, since it cannot be cured and also brings great complication to our health system. In this paper, we establish a general framework for explaining the functioning of Artificial Neural Networks (ANNs) in binomial classification and implement and evaluate the variants of Back propagation algorithm (Standard Back Propagation, Resilient – Back propagation, Variable Learning Rate, Powell-Beale Conjugate Gradient, Levenberg Marquardt, Quasi-Newton Algorithm and Scaled Conjugate Gradient) using Pima Indians Diabetes Data set from UCI repository of machine learning databases. We also compare Artificial Neural Networks (ANNs) with one of the conventional techniques, namely logistic regression (LR) to predict diabetic disease decisions.
References
R. Deja, W. Froelich, G. Deja, and A. Wakulicz-Deja, "Hybrid approach to the generation of medical guidelines for insulin therapy for children," Inf. Sci. (Ny)., vol. 384, pp. 157–173, 2017.
Y. Li, C. Bai, and C. K. Reddy, "A distributed ensemble approach for mining healthcare data under privacy constraints," Inf. Sci. (Ny)., vol. 330, pp. 245–259, 2016.
J. A. Morente-Molinera, I. J. Pérez, M. R. Ureña, and E. Herrera-Viedma, "Creating knowledge databases for storing and sharing people knowledge automatically using group decision making and fuzzy ontologies," Inf. Sci. (Ny)., vol. 328, pp. 418–434, 2016.
World Health Organization, "Prevention of blindness from diabetes mellitus," Geneva WHO, pp. 1–48, 2005.
http://ftp.ics.uci.edu/pub/ml-repos/machine-learning databases/pimaindians-diabetes, 2003.
L. Breiman et al., "Classification and regression trees," Belmont, CA: Wadsworth, 1984.
S. Salzberg, "Book Review: C4.5: Programs for Machine Learning," Mach. Learn., vol. 1, no. 16, pp. 235–240, 1994.
C. Caruth, "History as false witness". In: Ekman, U., Tygstrup, F. (eds.) Witness Memory, Representation and the Media in Question. Museum Tusculanum Press, University of Copenhagen, 2008.
D. W. Hosmer and S. Lemeshow, "Applied Logistic Regression," Wiley Series in Probability and Statistics, no. 1, p. 373, 2000.
M. T. Hagan, H. B. Demuth, and M. H. Beale, "Neural Network Design," Bost. Massachusetts PWS, vol. 2, p. 734, 1995.
M. R. Guerriere, A. S. Detsky, "Neural networks: what are they? ", Ann Intern Med, 1991;115:906–907.
S. Haykin, "Neural networks: a comprehensive foundation", Upper Saddle River, NJ: Prentice Hall, 1998.
J. J. More, "Numerical Analysis," edited by Watson, G.A, Lecture Notes in Mathematics 630, (Springer Verlag, Germany) 1997,105-116.
M. T. Hagan and M. Menhaj. IEEE Trans Neural Networks, 5(6) (1994) 989-993.
S. A. Dudani. "The Distance-Weighted k-NN Rule," IEEE Transactions on Systems, Man and Cybernetics, Volume 6, Number 4, pp. 325-327, 1976.
Nahla H. Barakat, Andrew P. Bradley, and Mohamed Nabil H. Barakat, "Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus," IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 4, July 2010.
Val´erie Bourdes, St´ephane Bonnevay, Paolo Lisboa, R´emy Defrance, David P´erol, Sylvie Chabaud, Thomas Bachelot, Th´erese Gargi, and Sylvie N´egrier, "Comparison of Artificial Neural Network with Logistic Regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient Outcomes," Advances in Artificial Neural Systems, Volume 2010, Article ID 309841, 10 pages.
S. Sa'di, A. Maleki, R. Hashemi, Z. Panbechi, K. Chalabi. "Comparison of Data Mining Algorithms in the Diagnosis of Type II Diabetes," International Journal on Computational Science & Applications (IJCSA), Vol.5, No.5, (2015), pp. 1-12.
M. Adavi, M. Salehi, and M. Roudbari, "Artificial neural networks versus bivariate logistic regression in prediction diagnosis of patients with hypertension and diabetes," pp. 2–6, 2016.
S. Vijayarani, "Liver Disease Prediction using SVM and Naïve Bayes Algorithms," vol. 4, no. 4, pp. 816–820, 2015.
J. Li, D. Zhang, Y. Li, J. Wu, and B. Zhang, "Joint similar and specific learning for diabetes mellitus and impaired glucose regulation detection," Inf. Sci. (Ny)., vol. 384, pp. 191–204, 2017.
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