Identification of High-Risk Factors and Advanced Detection of Diabetes Utilizing a Hybrid Conv-LSTM Model

Abstract

Diabetes is a chronic disease that causes various damages to the human body, making early detection crucial. Hence, to address this issue, the current study utilizes hybrid convolutional long-short term memory (Conv-LSTM) Network which help to detect and classify diabetes at the early stages. The proposed Conv-LSTM enhances the model’s prediction by allowing CNN for spatial extraction of feature and LSTM for temporal extraction of feature from the input data. The proposed approach is applied to BRFSS dataset through the implementation of a computerized system for early identification of diabetes. The data gathered from the BRFSS dataset undergoes pre-processing step to ensure that it is suitable for further processing. The pre-processed data is then fed into the Conv-LSTM model which is trained to identify diabetes based on the risk factor. The efficacy of the proposed CGRU framework has been proven by validating the experimental findings with the existing state-of-the-art approaches. Compared to existing methods like machine learning, the proposed framework exhibited better performance. This demonstrates the efficacy of the Conv-LSTM architecture for diabetes prediction achieving high accuracy rate of 98.5%. The approach successfully identifies people who are at high risk of acquiring diabetes and achieves high accuracy in early diabetes detection, allowing for prompt intervention and individualized healthcare treatment.