Header menu link for other important links
KM-LA: knowledge-based mining for linear analysis of inconsistent medical data for healthcare applications
Published in Springer Science and Business Media Deutschland GmbH
Healthcare data analysis is a prominent field of research supporting information technologies in the medical industry. Handling large volumes of data and mining them for application-related services requires time-efficient and less complex processing. With the implication of machine learning in computing processes, the analysis systems and mining performance are improved. In this manuscript, knowledge-based mining with a linear analysis (KM-LA) model is presented. This analysis model relies on a knowledge base and definitive learning in handling big medical data for health application-centric services. This proposal aims to provide a definite linear solution for medical data mining through less complex analysis for simpler healthcare services. The analysis model is proposed to reduce the inconsistency in handling extensive medical data without causing service failures. The linear analysis model’s performance is verified using suitable experiments to verify service latency, analysis time, computation complexity, and inconsistency. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
About the journal
JournalData powered by TypesetPersonal and Ubiquitous Computing
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH