A Robust Method for Diabetes Detection Using Deep Learning with Convolutional LSTM Networks

Abstract: A common metabolic disease that can impact billions of individuals worldwide, diabetes mellitus is a chronic illness.

Diabetes complications can be adequately prevented with ongoing monitoring and early identification. In this study, we use

ConvLSTM networks and deep learning techniques to develop a reliable method for diabetes detection. Because of its

spatiotemporal properties, ConvLSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory

(LSTM), is well-suited for processing medical data. Compared to approaches created before it and other deep learning models,

our approach demonstrated to have superior accuracy and efficiency. In benchmark datasets, our algorithm proved efficacy in

early diabetes identification and categorization.