A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases. (January 2018) - Kumar, Priyan Malarvizhi; Devi Gandhi, Usha
Highlights: This paper proposes a scalable three-tier architecture to store and process such huge volume of wearable sensor data. Tier-1 focuses on collecting data from IoT wearable sensor devices. Tier-2 uses Apache HBase to store the huge volume of wearable IoT sensor data in the cloud. Tier-3 uses the Apache Mahout to develop the logistic regression-based prediction model for heart diseases. Abstract: Among the applications enabled by the Internet of Things (IoT), continuous health monitoring system is a particularly important one. Wearable sensor devices used in IoT health monitoring system have been generating an enormous amount of data on a continuous basis. The data generation speed of IoT sensor devices is very high. Hence, the volume of data generated from the IoT-based health monitoring system is also very high. In order to overcome this issue, this paper proposes a scalable three-tier architecture to store and process such huge volume of wearable sensor data. Tier-1 focuses on collection of data from IoT wearable sensor devices. Tier-2 uses Apache HBase for storing the large volume of wearable IoT sensor data in cloud computing. In addition, Tier-3 uses Apache Mahout for developing the logistic regression-based prediction model for heart diseases. Finally, ROC analysis is performed to identify the most significant clinical parameters to get heart disease. Graphical abstract: