Diabetes Prediction: A Deep Learning Approach

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Safial Islam Ayon 1,*

1. Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2019.02.03

Received: 21 Aug. 2018 / Revised: 26 Oct. 2018 / Accepted: 14 Dec. 2018 / Published: 8 Mar. 2019

Index Terms

Diabetes, Deep Neural Network (DNN), Prediction, Evaluation Metrics


Nowadays, Diabetes is one of the most common and severe diseases in Bangladesh as well as all over the world. It is not only harmful to the blood but also causes different kinds of diseases like blindness, renal disease, kidney problem, heart diseases etc. that causes a lot of death per year. So, it badly needs to develop a system that can effectively diagnose the diabetes patients using medical details. We propose a strategy for the diagnosis of diabetes using deep neural network by training its attributes in five and ten-fold cross-validation fashion. The Pima Indian Diabetes (PID) data set is retrieved from the UCI machine learning repository database. The results on PID dataset demonstrate that deep learning approach design an auspicious system for the prediction of diabetes with prediction accuracy of 98.35%, F1 score of 98, and MCC of 97 for five-fold cross-validation. Additionally, accuracy of 97.11%, sensitivity of 96.25%, and specificity of 98.80% are obtained for ten-fold cross-validation. The experimental results exhibit that the proposed system provides promising results in case of five-fold cross-validation.

Cite This Paper

Safial Islam Ayon, Md. Milon Islam, "Diabetes Prediction: A Deep Learning Approach", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.2, pp. 21-27, 2019. DOI:10.5815/ijieeb.2019.02.03


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