Asian Journal of Research in Computer Science, .,Vol.: 1, Issue.: 1
Diabetes Diagnosis Using Fuzzy – Neuro Hybrid Control Model
Danladi Ali1* 1Department of Pure and Applied Physics, Adamawa State University, Mubi, Nigeria.
1Department of Pure and Applied Physics, Adamawa State University, Mubi, Nigeria.
(1) Mohamed Elhoseny, Assistant Professor, Faculty of Computers and Information, Mansoura University, Egypt and Scientific Research Group in Egypt, Cairo University, Egypt.
(1) Nihal Taş, Department of Mathematics, Balikesir University, Turkey.
(2) Lijun Zhang, University of Science and Technology Beijing, China.
Complete Peer review History: http://www.sciencedomain.org/review-history/24608
Diabetes is caused due to an inability of a body to produce or respond to hormone insulin causing abnormal metabolism of carbohydrate which can lead to rising in sugar level in the blood. This work proposed a fuzzy - neuro hybrid control model to diagnose diabetes in terms of seven symptoms such as an increase in urination, increase in thirst, increase in fatigue, tingling in hands/feet, blurred vision, sores slow to heal and significant loss of weight. 15 patients were diagnosed with sugar levels as followed 9.6 mmol/l, 6.8 mmol/l, 9.1 mmol/l, 11.2 mmol/l, 6.5 mmol/l, 5.7 mmol/l, 11.8mmol/l, 8.9 mmol/l, 7.0 mmol/l, 11.0 mmol/l, 8.5 mmol/l, 9.0mmol/l, 12.4 mmol/l, 9.5 mmol/l and 10.4 mmol/l. The average diagnosis error is obtained as 0.05%, which is acceptable in medical diagnosis. In this regards, it is recommended that fuzzy- neuro hybrid control model is a good soft computing tool for diagnosing diabetes.
Diabetes; soft computing; fuzzy logic; neural network; sugar level; expert domain.
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