RULE EMBEDDED SEMANTIC ONTOLOGY BASED CLASSIFIERFOR IoTHEALTHCARE

Abstract

Internet of Things (IoT) is an emerging technology in all domains that generates large amounts of
data at rapid pace. TheIoT devices are interconnected in a way to communicate and share data with
each other. Knowledge mining from such large amounts of data is a difficult task. So commonly, data
analytics modelsareused to extract knowledge. However, most data are not fully utilized because of
their dynamic problems and difficulties in analyzing data collected fromdiverse resources. To
overcome the above stated issues, semantic technologies are used to provide a common model to
handle the data. In the field of healthcare, predicting the patient's disease accurately is one of the most
important considerations. For this, semantic data is very useful to make accurate predictions quickly
with minimal cost. In this paper, asemantic ontology based technique has beenproposed for IoT based
healthcare domain. The proposed technique Rule Embedded Semantic Ontology Classifier (RESOC)is
implementedin two steps,namely data collection and semantic enrichment. Data is collected through
various sources and then the RESOC is developed in the semantic enrichment phase. Finally,
theenriched semantic data enables theDeep neural Network (DNN)for disease classification. The
resultsarecompared based on certain parameters such as precision, recall, F-score and accuracy.
Hence, the semantically enriched ontology handles heterogeneity and improves classification
accuracy.