Small-World Inspired Neural Network (SW-NN) for an Effective and Accurate Diagnosis of Diabetes

Abstract

As the use of artificial intelligence in medical diagnosis is growing, the need for enhanced efficacy is becoming paramount. By merging the robustness of neural networks with the distinctive attributes of small-world networks (SWN), the possibility arises for even greater levels of accuracy with maintaining good generalizability. This work presents a novel approach for diagnosing diabetes using a Small-World inspired Neural Network (SW-NN). By constructing a SW-NN using Newman-Watts algorithm, we employ its architecture to classify diabetes and compare it to a conventional NN. The SW-NN outperforms the conventional NN in classification accuracy and evaluation metrics as shown by the experimental results. Our accuracy analysis conclusively demonstrates the superiority of the SW-NN, highlighting its potential to improve diabetes diagnosis. © 2023 IEEE.