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CLASSIFICATION OF PREDIABETES AND TYPE 2 DIABETES USING ARTIFICIAL NEURAL NETWORK

2017; Springer Nature; Linguagem: Inglês

10.1007/978-981-10-4166-2_103

1727-1983

Dijana Sejdinović, Lejla Gurbeta, Almir Badnjević, Maja Malenica, Tanja Dujić, Adlija Čaušević, Tamer Bego, Lejla Divović Mehmedović,

ECG Monitoring and Analysis

In this paper development of Artificial Neural Network for classification of prediabetes and type 2 diabetes (T2D) is presented. For development of this system 310 samples consisting of information about Fasting Plasma Glucose (FPG) and blood test called HbA1c were used. All samples were obtained from several healthcare institutions in Bosnia and Herzegovina, and diagnosis of prediabetes, T2D and healthy patients in this dataset were established by medical professionals. Two-layer feedforward backpropagation network with 15 neurons in hidden layer and sigmoid transfer function, used for classification of prediabetes and T2D in this paper, was trained with 190 samples. Testing of developed neural network was performed with 120 samples for validation also obtained from healthcare institutions in Bosnia and Herzegovina. Out of 120 samples, developed network was accurate in 94.1% cases for the classification of prediabetes and in 93.3% cases for classification of T2D.