Diabetes Diagnosis Using Fuzzy – Neuro Hybrid Control Model

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Danladi Ali


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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How to Cite
Ali, D. (2018). Diabetes Diagnosis Using Fuzzy – Neuro Hybrid Control Model. Asian Journal of Research in Computer Science, 1(1), 1-12. https://doi.org/10.9734/ajrcos/2018/v1i124722
Original Research Article