Long Short-Term Memory Network for Accelerometer-Based Hypertension Classification

This study investigates the application of a Long Short-Term Memory (LSTM) architecture for classifying hypertension using accelerometer data, specifically focusing on physical activity and sleep from the publicly available NHANES 2011-2012 dataset. The LSTM model captures the sequential patterns in this data, providing insights into behavioral patterns related to hypertension. The performance of the LSTM model is compared to traditional machine learning methods as well as other commonly used sequence models, including Recurrent Neural Networks (RNN), Transformers (TF), and 1D Convolutional Networks (Conv1D). The results show that the LSTM model achieves superior accuracy at 96.37%, outperforming the RNN (75.67%), TF (77.10%), and Conv1D (89.34%), as well as the other machine learning models, which range from 60.92% to 64.75%. These findings underscore the potential of LSTM models for integration into wearable health monitoring systems, enabling early detection or management of hypertension.