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A Rule-Based Monitoring System for Accurate Prediction of Diabetes: Monitoring System for Diabetes

A Rule-Based Monitoring System for Accurate Prediction of Diabetes: Monitoring System for Diabetes

Anand Kumar Srivastava, Yugal Kumar, Pradeep Kumar Singh
Copyright: © 2020 |Volume: 11 |Issue: 3 |Pages: 22
ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799806912|DOI: 10.4018/IJEHMC.2020070103
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MLA

Srivastava, Anand Kumar, et al. "A Rule-Based Monitoring System for Accurate Prediction of Diabetes: Monitoring System for Diabetes." IJEHMC vol.11, no.3 2020: pp.32-53. http://doi.org/10.4018/IJEHMC.2020070103

APA

Srivastava, A. K., Kumar, Y., & Singh, P. K. (2020). A Rule-Based Monitoring System for Accurate Prediction of Diabetes: Monitoring System for Diabetes. International Journal of E-Health and Medical Communications (IJEHMC), 11(3), 32-53. http://doi.org/10.4018/IJEHMC.2020070103

Chicago

Srivastava, Anand Kumar, Yugal Kumar, and Pradeep Kumar Singh. "A Rule-Based Monitoring System for Accurate Prediction of Diabetes: Monitoring System for Diabetes," International Journal of E-Health and Medical Communications (IJEHMC) 11, no.3: 32-53. http://doi.org/10.4018/IJEHMC.2020070103

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Abstract

Diabetes is a chronic disease that can affect the life of people due to high sugar level in their blood. The sugar level is increased due to a lack of production of insulin in the human body. Large numbers of people are affected with diabetes and it can increase tremendously due life style behavior. Diabetes can also affect the other human organs, like kidneys, hearts, retinas and lead to the failure of these organs. This article presents a diabetic monitoring system to determine the risk of diabetes based on the personal health record of patients. In this work, several rules are designed based on the clinical as well as non-clinical symptoms. The effectiveness of the diabetes monitoring system is tested on a set of two hundred forty people. The simulation results are also compared with well-known techniques available for diabetes prediction. It is stated that proposed monitoring system obtains 90.41% accuracy rate as compared with other techniques.