LogNNet Neural Network Application for Diabetes Mellitus Diagnosis

Creative Commons License

Izotov Y., HUYUT M. T., Velichko A.

4th International Conference on Agricultural Engineering and Green Infrastructure for Sustainable Development, AEGISD 2024, Tashkent, Özbekistan, 28 - 30 Mart 2024, cilt.105 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 105
  • Doi Numarası: 10.1051/bioconf/202410502003
  • Basıldığı Şehir: Tashkent
  • Basıldığı Ülke: Özbekistan
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Evet

Özet

The paper presents a LogNNet neural network algorithm for diabetes mellitus diagnosing based on a public dataset. The study used 100 thousand records of patient conditions. Model quality was evaluated using the Matthews Correlation Coefficient metric (MCC). The LogNNet neural network model showed high accuracy (MCC=0.733) in diabetes mellitus recognition. A highly positive relationship between HbA1c level and glucose level in the disease diagnosing was found using the LogNNet model. It has been observed that evaluating these variables together is much more effective than their individual effects in diagnosing the disease.