An ANN Model for Early Prediction of Diabetes
Abstract
Diabetes a metabolic disease with the botanical name diabetes mellitus is diagnosed in a person who has high sugar levels in the bloodstream which could be either because of the cells not reacting to the insulin that is created or the pancreas does not deliver insulin by any stretch of the imagination. According to research (World Health Organization, Geneva 2014), Worldwide 194 million people have already been diagnosed with diabetes and this rate is expanding quickly and is estimated to achieve 333 million by 2025. In Africa, more than 5 million people are already diagnosed with diabetes, and it has been estimated that by 2025 diabetes patients in the continent would be up to 15 million. (KMV Narayan). Ogbera also reported that in Nigeria patients confirmed diabetes up to 158 million, and as such, the need to study the prediction of diabetes is very paramount. In this way, there is an incredible need to concentrate on the prediction of diabetes. So that precautions can be taken to control this deadly disease. This research work wishes to present the prediction of diabetes using ANN. The model used in this work considered a group of fifteen factors and identified the factors that are very influential in the diagnosis of diabetes using regression analysis so as to achieve better accuracy of prediction.
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Derouich M, Boutayeb A. (2002) the effect of physical exercise on the dynamics of glucose and Insulin.
Journal of Biomechanics.
Eng Khaled Eskaf, Prof. Dr. Osama Badawi and Prof. Dr. Tim Ritchings, Predicting blood
glucose levels in diabetes using feature extraction and artificial neural networks.
Gannous AS, Elhaddad YR. (2011) Improving an Artificial Neural Network Model to Predict
Thyroid Bending Protein Diagnosis Using Preprocessing Techniques. WASET.
Geneva, The world health report today’s challenges. World Health Organization.
http://www.who.int/whr/2003/en.
Gurney, K. (1997). An Introduction to Neural Networks, Routledge, ISBN 1-85728-673-1.
Lippmann, R.P. (1987). An introduction to computing with neural networks. IEEE Accost. Speech
Signal Process. Mag.
Liszka Hackzell, J. J. (April 1999) Prediction of blood glucose levels in diabetic patients using a
hybrid AI technique. Computers and Biomedical Research London.
Nahla H. Barakat, Andrew P. Bradley, Senior Member, IEEE, and Mohamed Nabil H. Barakat
(2010) Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus‖, IEEE
Transactions on Information Technology in Biomedicine, VOL. 14.
Shankaracharya, DevangOdedra, SubirSamanta, and Ambarish S. Vidyarthi (2011), Computational
Intelligence in Early Diabetes Diagnosis: A Review‖ Journal of the society for Biomedical
Diabetes Research.
T.Jayalakshmi and Dr.A.Santhakumaran, (2010) A novel classification method for classification
of diabetes mellitus using artificial neural networks. International Conference on Data
Storage and Data Engineering.
Uttreshwar, G.S. Ghatol, A.A. (2009) Hepatitis B Diagnosis Using Logical Inference and
Generalized Regression Neural Networks, IEEE International Advance Computing Conference.
Wilding P, Morgan M, Grygotis A, Shoffner M, Rosato E. (1994) Application of back
propagation neural networks to diagnosis of breast and ovarian cancer. Cancer Lett.
DOI: https://doi.org/10.26483/ijarcs.v13i6.6916
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