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Asian Journal of Research in Computer Science, .,Vol.: 1, Issue.: 1

Original-research-article

Diabetes Diagnosis Using Fuzzy – Neuro Hybrid Control Model

 

Danladi Ali1*

1Department of Pure and Applied Physics, Adamawa State University, Mubi, Nigeria.

Article Information

Editor(s):

(1) Mohamed Elhoseny, Assistant Professor, Faculty of Computers and Information, Mansoura University, Egypt and Scientific Research Group in Egypt, Cairo University, Egypt.

Reviewers:

(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

Abstracts

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.

Keywords :

Diabetes; soft computing; fuzzy logic; neural network; sugar level; expert domain.

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