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In this paper, we develop a scalable system which can do real-time analyticsfor different health applications. The occurrence of different healthconditions can be regarded as the complex events and thus this concept can beextended to other use cases easily. Large number of users should be able tosend the data in real-time, and should be able to receive the feedback andresult. Keeping the requirements in mind we used Kafka and Spark to develop oursystem. In this setting, multiple users are running Kafka producer clients,which are sending data in real-time. Spark streaming is used to process datafrom Kafka of different window sizes to analyze the health conditions. We havedeveloped and tested the heart attack risk and stress prediction as our samplecomplex events detection use cases. We have simulated and tested our systemwith multiple health datasets.

Complex Event Processing of Health Data in Real-time to Predict Heart Failure Risk and Stress