Detection of Renovascular Hypertension using Hybrid Deep Learning Technique

Abstract

Renovascular hypertension is a type of secondary hypertension that is difficult to diagnose and monitor due to its complexity and dependence on many weak bodies, resulting from the narrowing of the arteries feeding the kidney. This study presents a novel hybrid deep learning model that combines convolutional neural network (CNN) and short-term temporal (LSTM) network to predict renovascular blood pressure based on data. The proposed model uses the spatial feature extraction capabilities of CNNs to identify significant patterns in various health data such as heart rate, blood pressure, and blood oxygen, while the LSTM network manages physical time to monitor specific patients in real time. IoT devices continuously collect medical data and instantly transmit it to the model, helping to predict renovascular hypertension early and accurately. Experimental results demonstrate the effectiveness of the model with over 90% accuracy in predicting the onset of hypertension compared to deep learning models. Furthermore, this hybrid CNN-LSTM approach performs well in handling noisy and incomplete data that are common in the IoT environment. This study demonstrates the potential of a hybrid deep learning model to improve the content of care, personalize treatment, and provide large-scale, cost-effective solutions for early intervention in the management of renovascular hypertension.