LAUSR

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.