International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 11, No. 2, 8 Apr. 2021
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Predictive Intelligent Decision Support Model in Forecasting of the Diabetes Pandemic Using a Reinforcement Deep Learning Approach
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Author(s)
Index Terms
Diabetes, Type-I, Type-II, Gestational, deep neural network, modular learning, Silent killer
Abstract
Diabetes has since become global pandemic – which must be diagnosed early enough if the patients are to survive a while longer. Traditional means of detection has its limitations and defects. The adoption of data mining tools and adaptation of machine intelligence is to yield an approach of predictive diagnosis that offers solution to task, which traditional means do not proffer low-cost-effective results. The significance thus, is to investigate data feats rippled with ambiguities and noise as well as simulate model tractability in order to yield a low-cost and robust solution. Thus, we explore a deep learning ensemble for detection of diabetes as a decision support. Model achieved a 95-percent accuracy, with a sensitivity of 0.98. It also agrees with other studies that age, obesity, environ-conditions and family relation to the first/second degrees are critical factors to be watched for type-I and type-II management. While, mothers with/without previous case of gestational diabetes is confirmed if there is: (a) history of babies with weight above 4.5kg at birth, (b) resistant to insulin showing polycystic ovary syndrome, and (c) have abnormal tolerance to insulin.
Cite This Paper
Arnold Adimabua Ojugo, Elohor Ekurume, " Predictive Intelligent Decision Support Model in Forecasting of the Diabetes Pandemic Using a Reinforcement Deep Learning Approach", International Journal of Education and Management Engineering (IJEME), Vol.11, No.2, pp. 40-48, 2021. DOI: 10.5815/ijeme.2021.02.05
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