A Computational-Intelligence Based Approach to Diagnosis of Diabetes Mellitus Disease


DOĞU E. , ALBAYRAK Y. E.

13th International Conference on Application of Fuzzy Systems and Soft Computing (ICAFS), Warszawa, Polonya, 27 - 28 Ağustos 2018, cilt.896, ss.154-159 identifier identifier

  • Cilt numarası: 896
  • Doi Numarası: 10.1007/978-3-030-04164-9_22
  • Basıldığı Şehir: Warszawa
  • Basıldığı Ülke: Polonya
  • Sayfa Sayıları: ss.154-159

Özet

Diabetes Mellitus (DM) is a disease that occurs when the pancreas cannot produce enough insulin or when insulin that it produces cannot be used effectively. High frequency of urination and hunger and thirst are general symptoms of high levels of blood glucose. Global estimates of 2015 claims that 415 million people are living with diabetes and 90% of them belongs to Type 2 DM. DM have equal rates for men and woman, and a rate of 8.3% in total adults. Diagnosis of the disease is not challenging however, it requires blood glucose measurements in different times. In emergency cases where the patient is unconscious, the possibility to overlook the disease is high. In this study, fuzzy cmeans clustering algorithm, in which each variable can belong to more than one class, is used to classify the two groups of patients with and without diabetes through other blood test data and demographic factors. In the first application with 100 patients of a hospital, the algorithm correctly classified 81% of patients.