Abstract
Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin. The main aim of this research work was to determine the blood glucose level of diabetic patient using adaptive Neuro-fuzzy. Data of 80 diabetic patients were collected from Federal Medical Centre Jalingo. It was used for training and testing the system, Gaussian Membership function was used, hybrid training algorithm was used for training and testing, the error obtain is 0.0008333 at epoch 4 which shows that the training performance is exactly 99.99% and testing performance of the system are 99.99% at epoch 4. This shows that adaptive Neuro-fuzzy system can be applied to medical diagnosis because of the error obtained.
Highlights
Diabetes mellitus is a metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both
Computational intelligence, Artificial intelligence and soft computing techniques which includes; Artificial Neural network, fuzzy logic, evolutionary algorithms and swarm intelligence, or any combination of these that is Neuro-fuzzy, Neuro-genetics among others are widely applied to medical diagnosis, prognosis, determining severity level, controlling and predicting of any disease in recent time
Medical diagnosis largely depends on experience of the medical expert together with the results from medical laboratory scientist and the symptoms exhibit by the patient before concluding or confirming the presence of the disease
Summary
Diabetes mellitus is a metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. Type I is medically characterized by lack of insulin while type II is medically characterized by ineffective to utilize the insulin produce by the body [2]. Medical diagnosis largely depends on experience of the medical expert together with the results from medical laboratory scientist and the symptoms exhibit by the patient before concluding or confirming the presence of the disease. Computational intelligence/soft computing techniques follow similar procedure as medical expertise ; the symptoms exhibit by the patient, the results collected from lab and the experience of the medical expert are assign with weights or translate into a numeric values and formulate a matrix which would be used as an inputs parameters to confirm whether or not the presence of the disease
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