Real-Time Blood Pressure Prediction Using Apache Spark and Kafka Machine Learning

Using a mix of machine learning algorithms and big data tools, particularly Apache Spark and also Apache Kafka, this research provides a new method for real-time blood pressure prediction. The method can handle large amounts of inbound data from numerous sources, including wearable technology and internet of things monitors. A clustering-based approach is used to improve the blood pressure estimation's precision while the data is being analyzed in real-time. ECG, PPG, and ABP signals dataset are used to assess the suggested strategy, and the findings show a substantial improvement in blood pressure prediction accuracy when compared to previous methods. The suggested method has the potential to be used in numerous uses, such as remote patient tracking, individualized healthcare, and cardiovascular disease early detection. This research offers two contributions. First off, it introduces a novel technique for real-time blood pressure forecast that is more accurate than current approaches. In addition, it shows the value of merging machine learning techniques with real-time streaming data processing systems like Apache Spark and Apache Kafka. Further improving the scalability and accuracy of the system is the use of web-based tools and deep learning methods. The suggested method may have a big impact on how well patients do and how much it will cost to treat them. Overall, this research offers a path that can be useful to both individuals and healthcare professionals for the creation of real-time blood pressure forecast tools.