The Application of a Neural Network in the Reworking of Accu-Chek to Wrist Bands to Monitor Blood Glucose in the Human Body
The issue of high blood sugar level, the effects of which might end up as diabetes mellitus, is now becoming a rampant cardiovascular disorder in our community. In recent times, a lack of awareness among most people makes this disease a silent killer. The situation calls for urgency, hence the need to design a device that serves as a monitoring tool such as a wrist watch to give an alert of the danger a head of time to those living with high blood glucose, as well as to introduce a mechanism for checks and balances. The neural network architecture assumed 8-15-10 configuration with eight neurons at the input stage including a bias, 15 neurons at the hidden layer at the processing stage, and 10 neurons at the output stage indicating likely symptoms cases. The inputs are formed using the exclusive OR (XOR), with the expectation of getting an XOR output as the threshold value for diabetic symptom cases. The neural algorithm is coded in Java language with 1000 epoch runs to bring the errors into the barest minimum. The internal circuitry of the device comprises the compatible hardware requirement that matches the nature of each of the input neurons. The light emitting diodes (LED) of red, green, and yellow colors are used as the output for the neural network to show pattern recognition for severe cases, pre-hypertensive cases and normal without the traces of diabetes mellitus. The research concluded that neural network is an efficient Accu-Chek design tool for the proper monitoring of high glucose levels than the conventional methods of carrying out blood test.
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 World Health Organization, About diabetes on 31 March 2014.
 Kitabchi, A. E., Umpierrez, G. E., Miles, J. M. and Fisher, J. N. (2009). “Hyperglycemic crises in adult patients with diabetes.” diabetes Care. 32(7): 1335-43. PMC 2699725.
 Wild, S., Roglic, G., Green, A., Sicree, R. and King, H. (2004). Global prevalence of diabetes: Estimates for the year 2000 and pro#ections for 2030. diabetes Care, 27, 1047–1053.
 Davidson, N. K. and Moreland, P. (2011). Living with diabetes blog. mayoclinic.com. diabetes FAQs – Blood Glucose Measurement Units – Abbott diabetes Care What are mg/dl and mmol/l? How to convert? Glucose? Cholesterol? Advameg, Inc. "Screening for Type 2 diabetes". Clinical diabetes. 18 (2). 2000. Glucose test – blood. NIH – National Institutes of Health.
 Van Soest, P. J. (1994). Nutritional ecology of the ruminant. 2nd Ed. Cornell Univ. Press, ISBN 080142772X.
 American diabetes Association, (2006). "January 2006 diabetes Care". diabetes Care. 29 (Supplement 1): 51–580. PMID 16373931.
 Hao, Y.; Foster, R. (2008). “Wireless body sensor networks for health-monitoring applications. Phys. Meas. 29, R27–R56.
 Ashraf T, Panhwar Z, Habib S, Memon MA, Shamsi F, Arif J(2010). J Pak Med Assoc, 60(10):817-9.
 Susan, J. (2017). “Effect if diabetes on blood pressure and pulse”. PMID: 21381609.
 Wang, Y.R. and Margolis, D. (2006). The prevalence of diagnosed cutaneous manifestations during ambulatory diabetes visits in the United States, 1998–2002. Dermatology: 212:229–34
 Boulton, A. J., Vileikyte, L., Ragnarson-Tennvall, G. and Apelqvist, J. (2005). The global burden of diabetic foot disease. Lancet; 366:1719–24.
 Graupe, D. (2013). Principles of Artificial Neural Networks. World Scientific. pp. 1–. ISBN 978-981-4522-74-8.