Semantic IoT Interoperability and Data
Analytics Using Machine Learning
in Healthcare Sector
Pratiyush Guleria and Manu Sood
Abstract With the exponential growth of data in electronic form, it becomes a
complex and tedious task to extract meaningful information. The vast collection of
data has resulted in big data that may be in indeterminate form. The challenge is
to extract meaningful data from internet sources that are spreading across multiple
domains and to enable consistent resource sharing, interoperability on multiple IoT
platforms. The use of emerging technologies like Machine Learning and IoT is realized on multiple platforms, systems, and service applications. The introduction of
predefined libraries on Natural Language Processing in Machine learning platforms
has emphasized on the semantic web technologies and its IoT future directions. In
this chapter, authors have discussed the role of the semantic web, three layered framework for IoT interoperability, and have framed a web ontology structure for semantic
interoperability in IoT for the healthcare sector. Authors have also proposed the text
analytics model for the healthcare sector and performed semantic data classification on synthesized healthcare dataset to predict the patient diagnosis using Machine
learning techniques.
Keywords Healthcare · IoT · Learning · Machine · Python · Semantic · Structured
1 Introduction
The semantic technologies involve technologies from Artificial Intelligence to
Natural Language Processing. The NLP field involves the linked data and linguistic
web. In the Semantic web, information is connected and linked from one source to
another in the form of linked lists. Semantic technologies result in the evolution of
Internet technologies. Machine Learning is the emerging field where one focus of
research is on Natural Language Processing and predicting the results through past
P. Guleria (B)
NIELIT Shimla, Shimla, Himachal Pradesh, India
e-mail: pratiyushguleria@gmail.com
M. Sood
Department of Computer Science, Himachal Pradesh University, Shimla, India
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_11
245
Analytics Using Machine Learning
in Healthcare Sector
Pratiyush Guleria and Manu Sood
Abstract With the exponential growth of data in electronic form, it becomes a
complex and tedious task to extract meaningful information. The vast collection of
data has resulted in big data that may be in indeterminate form. The challenge is
to extract meaningful data from internet sources that are spreading across multiple
domains and to enable consistent resource sharing, interoperability on multiple IoT
platforms. The use of emerging technologies like Machine Learning and IoT is realized on multiple platforms, systems, and service applications. The introduction of
predefined libraries on Natural Language Processing in Machine learning platforms
has emphasized on the semantic web technologies and its IoT future directions. In
this chapter, authors have discussed the role of the semantic web, three layered framework for IoT interoperability, and have framed a web ontology structure for semantic
interoperability in IoT for the healthcare sector. Authors have also proposed the text
analytics model for the healthcare sector and performed semantic data classification on synthesized healthcare dataset to predict the patient diagnosis using Machine
learning techniques.
Keywords Healthcare · IoT · Learning · Machine · Python · Semantic · Structured
1 Introduction
The semantic technologies involve technologies from Artificial Intelligence to
Natural Language Processing. The NLP field involves the linked data and linguistic
web. In the Semantic web, information is connected and linked from one source to
another in the form of linked lists. Semantic technologies result in the evolution of
Internet technologies. Machine Learning is the emerging field where one focus of
research is on Natural Language Processing and predicting the results through past
P. Guleria (B)
NIELIT Shimla, Shimla, Himachal Pradesh, India
e-mail: pratiyushguleria@gmail.com
M. Sood
Department of Computer Science, Himachal Pradesh University, Shimla, India
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_11
245