Diagnosing Diabetes Using Artificial Neural Networks

Joy Oyinye Orukwo and Ledisi Giok Kabari
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Joy Oyinye Orukwo: Ignatius Ajuru University of Education, Port Harcourt, Nigeria.
Ledisi Giok Kabari: Ken Saro-Wiwa Polytechnic, Bori, Nigeria

European Journal of Engineering and Technology Research, 2020, vol. 5, issue 2, 221-224

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 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:5:y:2020:i:2:id:61774

DOI: 10.24018/ejeng.2020.5.2.1774

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