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Real-time machine learning for early detection of heart disease using big data approach


Using Big Data Analytics to Cr

Scalable and Secure Big Data I

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Abstract


Over the last few decades, heart disease is the most common cause of global death. So early detection of heart disease and continuous monitoring can reduce the mortality rate. The exponential growth of data from different sources such as wearable sensor devices used in Internet of Things health monitoring, streaming system and others have been generating an enormous amount of data on a continuous basis. The combination of streaming big data analytics and machine learning is a breakthrough technology that can have a significant impact in healthcare field especially early detection of heart disease. This technology can be more powerful and less expensive. To overcome this issue, this paper propose a realtime heart disease prediction system based on apache Spark which stand as a strong large scale distributed computing platform that can be used successfully for streaming data event against machine learning through in-memory computations. The system consists of two main sub parts, namely streaming processing and data storage and visualization. The first uses Spark MLlib with Spark streaming and applies classification model on data events to predict heart disease. The seconds uses Apache Cassandra for storing the large volume of generated data

KeyWords
real-time, distributed machine learning, big data, spark, heart disease, MLlib



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