A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases. (January 2018)

• Record Type: Journal Article
• Authors: Kumar, Priyan Malarvizhi; Devi Gandhi, Usha
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 222
• Page End: 235
• Publication Date: 2018-01
• Subjects: Internet of Things -- Machine learning -- Wearable IoT devices -- Big data -- ROC analysis; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.09.001
• Languages: English
• ISSNs: 0045-7906

Abstract

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: