Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/5168
Title: A Rule-Based Monitoring System for Accurate Prediction of Diabetes Monitoring System for Diabetes
Authors: Srivastava, Anand Kumar
Yugal Kumar
Singh, Pradeep Kumar
Keywords: Diabetes
Machine Learning
Monitoring System
PHR
Issue Date: 2020
Publisher: Jaypee University of Information Technology, Solan, H.P.
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.
Description: International Journal of E-Health and Medical Communications Volume 11 • Issue 3 • July-September 2020
URI: http://ir.juit.ac.in:8080/jspui//xmlui/handle/123456789/5168
Appears in Collections:Journal Articles



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