Abstract:
learn Diabetes or Diabetes mellitus is one of the major public health problems in the
world, and it arises at the pancreas does not supply sufficient insulin or the body unable
to use that insulin effectively. Although there is no definitive cure for this disease,
accurate detection is very important since diabetes causes heart attack and stroke, and
damage to the kidney, eyes, nerve, etc. Nowadays, many researchers have engaged in
identifying diabetes disease with numerous Artificial Intelligence (AI) techniques due to
the complexity of the problem. This study discovered that the Diabetes identification
ability of the Deep Learning Neural Network together with different optimizers, namely
Adam, SGD, RMSprop, and Adagrad. Moreover, stratified 5-fold Cross-validation was
applied to learn the model referring to the Pima Indian Diabetes Dataset (PIDD) which is
an imbalanced limited data set. The performance accuracy of the optimizers was
compared by using the Area Under Curve (AUC) score of the Receiver Operating
Characteristic (ROC) curve. In addition, Sensitivity, Specificity, Balanced accuracy,
precision, and F1-Score measurements were used to compare the classification accuracy
of the predictions. The findings of this study revealed that the Adams optimizer obtained
the best results in the diabetes classification by using the DNN model with imbalanced
data set. Meanwhile, AdaGrad optimizer scored the lowest results.
