LAUSR

High systolic blood pressure causes many problems, including stroke, brain attack, and others. Therefore, examining blood pressure and discovering issues related to it at the right time can help prevent the occurrence of health problems. Nowadays, health-based data brings a new dimension to healthcare by exploiting the real-time patients’ data to early detect systolic blood pressure (SBP). Furthermore, technologies typically associated with smart and real-time data processing add value in the healthcaredomain, including artificial intelligence, data analytic technologies, and stream processing technologies. Thus, this paper introduces a systolic blood pressure prediction system that can predict SBP in real-time and, therefore, can avoid health problems that may stem from sudden high blood pressure. The proposed system works through two components, namely, developing an offline model and an online prediction pipeline. The aim of developing an offline model module is to develop the model using investigate different deep learning models to achieve the smallest root mean square error. It has been developed using Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Short-Term Memory (BI-LSTM), Gated Recurrent Units (GRU) models andMedical Information Mart for Intensive Care (MIMC II) SBP time-series dataset. The online prediction pipeline module is using Apache Kafka and Apache Spark to predict the near future of SBP in real-time using the best deep learning model and SBP streaming time-series data. The experimental results indicate that the BI-LSTM model has achieved the best performance using three hidden layers, and it is used to predict the near future of SBP in real-time.