Enhanced Early Detection of Diabetic Nephropathy Using a Hybrid Autoencoder-LSTM Model for Clinical Prediction

Abstract

Early detection and precise prediction are essential in medical diagnosis, particularly for diseases such as diabetic nephropathy (DN), which tends to go undiagnosed at its early stages. Conventional diagnostic techniques may not be sensitive and timely, and hence, early intervention might be difficult. This research delves into the application of a hybrid Autoencoder-LSTM model to improve DN detection. The Autoencoder (AE) unit compresses clinical data with preservation of important features and dimensionality reduction. The Long Short-Term Memory (LSTM) network subsequently processes temporal patterns and sequential dependency, enhancing feature learning for timely diagnosis. Clinical and demographic information from diabetic patients are included in the dataset, evaluating variables such as age, sex, type of diabetes, duration of disease, smoking, and alcohol use. The model is done using Python and exhibits better performance compared to conventional methods. The Hybrid AE-LSTM model proposed here attains an accuracy of 99.2%, which is a 6.68% improvement over Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression. The findings demonstrate the power of deep learning in detecting DN early and accurately and present a novel tool for proactive disease control among diabetic patients.