Big Health Data: Cardiovascular Disease Prevention Using Big Data and Machine Learning

Abstract

By 2030, almost 23.6 million people will die from cardiovascular heart diseases (CVDs). In Morocco, deaths by CVDs represented 38% in 2018. Using machine learning and tracking patient health indicators can reduce this mortality. Indeed, the aim of this study is developing an application that collects and process a stream of geolocation and heart rate data, stores the data and predicts on cardiovascular heart diseases risk. We first construct the machine learning model, define the architecture then we developed and tested the data pipeline. Samsung smartwatch was used to collect heart rate and location, Kafka and Spark were used to collect the streamed data received from the Smartwatch, the Data was then stored in MongoDB. This work produced a development of a complete real time data pipeline from data production to alerts and reports generation using big data and machine learning technologies.