Cardiovascular Disorder Detection in Diabetes Mellitus Patients: An Integrated VGG and Bi-LSTM Model Optimized Using the ABC Algorithm

Abstract

There is a major public health concern at the intersection of Diabetes Mellitus (DM) and Cardiovascular Diseases (CVDs). Patients with a diabetes diagnosis are more likely to experience a variety of cardiovascular problems. Better patient outcomes and lower healthcare costs can result from early diagnosis of these problems. This study presents a fresh computational model to tackle this problem. This research presents an integrated method that optimizes the VGG and Bidirectional Long Short Tem Memory (Bi LSTM) models together with the help of the Artificial Bee Colony (ABC) algorithm, which is based on the swarm intelligence of artificial bees. Cardiac images are processed using the VGG network, which has been shown to be highly effective in image classification, while the Bi LSTM is optimized for processing time series data from medical sensors, such as heart rates and blood sugar levels. The selected characteristics are then used in the proposed VGG 16 model before being sent to Bi-LSTM for further processing and abnormality detection. The VGG consists of 16 layers, all of which are blocks of 2D Convolution and Max Pooling layers. The ABC method was created as a result of research into intelligent behavior and is now widely used in areas such as problem solving, categorization, and optimization. The ABC algorithm is used to the unified model, which results in improved adaptability, speed of convergence, and robustness. To better forecast cardiovascular diseases, this research presents an Integrated VGG16 model with Bi-LSTM model with ABC optimization (VGG-Bi-LSTM-ABC) to predict the cardiovascular disorders. When compared to the standard model, the proposed model's ability to detect disorder is much better. Preliminary results from a carefully selected dataset of DM patients show that the integrated model outperforms state-of-the-art approaches in key measures, further demonstrating the promise of Artificial Intelligence (AI)-driven advances in medical diagnosis.