Diagnosing Diabetes Using Artificial Neural Networks

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  •   Joy Oyinye Orukwo

  •   Ledisi Giok Kabari

Abstract

Diabetes has always been a silent killer and the number of people suffering from it has increased tremendously in the last few decades. More often than not, people continue with their normal lifestyle, unaware that their health is at severe risk and with each passing day diabetes goes undetected. Artificial Neural Networks have become extensively useful in medical diagnosis as it provides a powerful tool to help analyze, model and make sense of complex clinical data. This study developed a diabetes diagnosis system using feed-forward neural network with supervised learning algorithm. The neural network is systematically trained and tested and a success rate of 90% was achieved.


Keywords: Diabetes Mellitus, Neural Network, Feed Forward, Supervised Learning

References

Kelly, J. (2016). National Diabetes Education Program. Retrieved June 25, 2018, from https://www.cdc.gov/media/presskits/aahd/diabetes.pdf

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Diabetes Research and Wellbeing foundation. (2017). Retrieved June 23, 2018, from Diabetes Research and Wellbeing foundation: www.diabeteswellness.net

Pappada,S., Cameron, B., and Rosman,B. (2008). Development of a neural network for prediction of glocose concentration in type 1 diabetes patients. Journal of Diabetes Science and Technology, 2(5), 792-801.

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Divya, M., Raman, C., and Sumit, K. (2013). Diabetes Detection Using Artificial Neural Networks & Back-Propagation Algorithm. International Journal of Scientific and Technology Research, 2(1), 9-11.

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How to Cite
[1]
Orukwo, J.O. and Kabari, L.G. 2020. Diagnosing Diabetes Using Artificial Neural Networks. European Journal of Engineering and Technology Research. 5, 2 (Feb. 2020), 221–224. DOI:https://doi.org/10.24018/ejeng.2020.5.2.1774.