Random forest for big data classification in the internet of things using optimal features

• Published in: International journal of machine learning and cybernetics Vol. 10; no. 10; pp. 2609 - 2618
• Main Authors: Lakshmanaprabu, S. K., Shankar, K., Ilayaraja, M., Nasir, Abdul Wahid, Vijayakumar, V., Chilamkurti, Naveen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Big Data; Classification; Classifiers; Complex Systems; Computational Intelligence; Control; Data mining; Data models; Datasets; Disease; Engineering; Feature selection; Internet of Things; Mechatronics; Original Article; Pattern Recognition; Performance evaluation; Robotics; Semantics; Systems Biology; Internet of things; Random forest classifier; Big data; E-health; Dragonfly algorithm; Map reduce; Optimization
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-018-00916-z

The internet of things (IoT) is an internet among things through advanced communication without human’s operation. The effective use of data classification in IoT to find new and hidden truth can enhance the medical field. In this paper, the big data analytics on IoT based healthcare system is developed using the Random Forest Classifier (RFC) and MapReduce process. The e-health data are collected from the patients who suffered from different diseases is considered for analysis. The optimal attributes are chosen by using Improved Dragonfly Algorithm (IDA) from the database for the better classification. Finally, RFC classifier is used to classify the e-health data with the help of optimal features. It is observed from the implementation results is that the maximum precision of the proposed technique is 94.2%. In order to verify the effectiveness of the proposed method, the different performance measures are analyzed and compared with existing methods.