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.

医疗保健数据分析是支持医疗行业信息技术的重要研究领域。处理大量数据并将其挖掘以用于与应用程序相关的服务需要高效且省时的处理。随着机器学习在计算过程中的应用，分析系统和挖掘性能得到了改善。在此手稿中，介绍了基于知识的线性分析（KM-LA）挖掘。该分析模型依靠知识库和确定的学习来处理以医疗应用程序为中心的大医疗数据。该提案旨在通过更简单的分析为更简单的医疗服务提供医疗数据挖掘的确定线性解决方案。提出分析模型是为了减少处理大量医疗数据时的不一致性，而不会引起服务故障。使用适当的实验来验证线性分析模型的性能，以验证服务等待时间，分析时间，计算复杂性和不一致性。