Precision Unleashed: Enhancing Vital Sign Predictions Using Long-Short-Term Memory Networks

Precise vital sign prediction is essential to monitor and treat patients’ medical conditions. This paper provides a deep learning-based method using Long-Short-Term Memory (LSTM) networks to forecast the values of vital signs, particularly the body temperature and heart rate. To conduct a comparative study, the performance of the proposed LSTM model is compared to other prediction models, such as statistics and deep neural network models. The models’ performance assessment was performed using two vital sign datasets: the body temperature dataset from a publicly accessible dataset and the heart rate dataset from the MIT-BIH Arrhythmia Database. To determine how accurately the proposed model predictions were made, three evaluation measures are considered: mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). The experimental results illustrate that the proposed LSTM-based model outperforms the industry standard models in decreasing the prediction errors for heart rate and body temperature. The results of this study demonstrate how well LSTM networks capture temporal connections and accurately predict vital sign readings. Besides, the proposed strategy attains the potential boosting of patient monitoring systems and the standard of healthcare delivery.