Accurate Prediction of Type II Diabetes using Artificial Neutral Networks

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Published Oct 31, 2022
https://doi.org/10.47164/ijngc.v13i3.863
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Volume 13, Special Issue 3, October 2022

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Kiran Bala Dubey
MATS University, Raipur
Dr. Gyanesh Shrivastava
School of Information Technology, MATS University, Raipur, Chhattisgarh, India

Abstract

 Diabetes is a serious and progressive condition that is rapidly increasing in incidence and currently ranks third on the list of all causes of mortality throughout the globe. A key challenge for any nation, but particularly for one that is undergoing substantial change is the high diabetes prevalence rate. Research in the field of epidemiology has demonstrated that obesity and Type II diabetes are the result of a combination of genetic predisposition and lifestyle factors such as bad eating habits and a lack of physical exercise. This article presents machine learning and feature selection enabled framework for diabetes type 2 prediction. This article uses artificial neural network for classification and prediction of diabetes type 2 data. Input data used in experiment is gathered from Pima Indian Diabetes Dataset. Results are compared on the basis of certain parameters like- accuracy, sensitivity, specificity. Accuracy of artificial neural network is better for classification and prediction of type 2 diabetes.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Dubey, K. B., & Shrivastava, D. G. . (2022). Accurate Prediction of Type II Diabetes using Artificial Neutral Networks. International Journal of Next-Generation Computing, 13(3). https://doi.org/10.47164/ijngc.v13i3.863
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