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A Hybrid Model of Neural Network and Grey Wolf Optimization to Predict Diabetes

Sailaja Thota, Hamsini A Kumar, R. Jothsna, Krupa T Naik, V. Malavathi


Data mining is a process of identifying hidden patterns, modeling large amount of data to find relationships useful to data analyst. Now-a-days it has become a major popular technique in desperate research field due to its boundless approaches and applications. In this paper there is hybridization of two methods i.e, Grey Wolf Optimization (GWO) and Artificial Neural Network (ANN) for predicting diabetes (using PIMA Indian Dataset). Grey Wolf Optimization is a global search method and it helps artificial neural network to calculate initial optimal weights and biases and also enhances the performance of Back Propagation Neural Network (BPNN) by increasing convergence speed and better accuracy.


Diabetes, Artificial Neural Network, Grey Wolf Optimization, Back Propagation Neural Network.

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Amee Upadhyay, Vaishali R Patel ”Comparative Study-Prediction of Heart Disease and Diabetes using Data Mining Approaches”, International Journal of Engineering Technology, 2016, Vol 4, Issue 1, ISSN 2349-4476.

Hamza Turabieh “A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease”, American Journal of operations Reasearche, 2016, 6, 136-146.

Muhammad Akmal Sapon, Khadijah Ismail and Suehazlyn Zainudin on “Prediction of diabetes by using Artificial Neural Network”, 2011 International conference on circuits, IACSIT press, Singapore.

T S Antanam, Ms Padmavathi “Applications of K-means and Genetic algorithm for Dimensionality Reduction by integrated SVM for Diabetes Diagnosis”.

Kahramanli, H. and Allahverdi, N. “Design of a Hybrid System for diabetes and Heart Disease” (2008). Export Systems with Applications,35, 82-89

Manaswini Pradhan, Dr. Ranjit Kumar Sahu “Predict the onset of diabetes disease using Artificial Neural Network (ANN)” International Journal of Computer Science & Emerging Technologies (E-ISSN: 2044-6004) 303 Volume 2, Issue 2, April 2011.

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. Durairaj, G. Kalaiselvi “Prediction of Diabetes Using Soft Computing Techniques- A Survey” international journal of scientific & technology research volume 4, issue 03, march 2015.

Antonio SalgadoCastillo1, Tahimy González Rubio “Evaluation of ANN and SVM for the classification and prediction of patients with diabetic neuropathy”

Zahed Soltani, Urmia, Iran, Ahmad Jafarian “A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II”(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 6, 2016 89.

Muhammad Akmal Sapon 1, Khadijah Ismail1 and Suehazlyn Zainudin” Prediction of Diabetes by using Artificial Neural Network”2011 International Conference on Circuits, System and Simulation IPCSIT vol.7 (2011) © (2011) IACSIT Press, Singapore

E. O. Olaniyi, K. Adnan, “Onset Diabetes Diagnosis Using Artificial Neural Network”, International Journal of Scientific & Engineering Research, vol.5, issue 10,(2014), pp. 754-759.

S. Kumar, A. Kumaravel, “Diabetes Diagnosis using Artificial Neural Network”, International journal of engineering sciences & research technology, Vol.2, No.6, (2013), pp. 1642-1644.

S. Sa'di, R. Hashemi, A. Abdollapour, K. Chalabi, M. A. Salamat, “A Novel Probabilistic Artificial Neural Networks Approach for Diagnosing Heart Disease”, International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.6, (2015), pp.

“Survey on Anomaly Detection using Data Mining Techniques”, By Shikha Agrawal, Jitendra Agrawal at 19th International Conference on Knowledge based and Intelligent Information and Engineering Systems.

“A Survey of Mining Techniques for early lung cancer Diagnosis” by Dr. C. Chilambu Chelvan, Juliet Rani Rajan at International Conference on Green Computing, Communication and Conservation of Energy, 2013.

Muro C, Escobedo R, Spector L, Coppinger R. “Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav Process” 2011;88:192–7.


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