Abstract

Background: Recent research demonstrates that diabetes can lead to heart problems, neurological damage, and other illnesses.

Method: In this paper, we design a low-complexity Deep Learning (DL)-based model for the diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes Dataset (PIDD). To obtain a low-complexity and accurate DL architecture, we perform an accuracy-versus-complexity study on several DL models.

Result: The results show that the proposed DL structure, including Convolutional Neural Networks and Multi-Layer Perceptron models (i.e., CNN+MLP model) outperforms other models with an accuracy of 93.89%.

Conclusion: With these features, the proposed hybrid model can be used in wearable devices and IoT-based health monitoring applications.

Keywords: Complexity, diabetes, deep learning, internet of things.