A Comparative Framework for Diabetes Mellitus Diagnosis Using Artificial Neural Network Models

The growing global concern for the high prevalence of diabetes mellitus

has resulted in an increased focus on measures for its early and accurate diagnosis and strategies

for its control. Various machine learning and deep learning models can be utilized for accurate diabetes

diagnosis. This study focuses on designing a comparative framework for Diabetes Mellitus

detection using the deep learning model of Artificial Neural Networks (ANN). The study examines

the behaviour of the altering hyperparameters, learning rate, epochs, and the number of hidden

layers in an ANN, on the accuracy of diabetes mellitus prediction.

This study developed ANN models on a novel diabetes dataset for diabetes

mellitus prediction, and compared them on key performance metrics.

The results demonstrate that an ANN model with two and three hidden layers and learning

rates of 0.005 and 0.0005, respectively, achieved 88.0% accuracy and an AUC score above

0.8.

Interpreting the ROC Curve, the study found that small or very large epochs showed

lower accuracy in the dataset. Learning rates of 0.005 and 0.0005 demonstrated a higher performance

than the learning rate of 0.05.

A holistic plan for diabetes management requires integrating deep learning-based

ANN models for prediction, coupling them into clinical practice. Future studies would apply the

hybrid and ensemble algorithms to the novel dataset and compare the performance of the prediction.

Our research would contribute to the global effort in diabetes mellitus management, thus reducing

the overall cost of this life-long disease.