Efficient Tracing and Detection of Activity Deviation in Event Log Using ProM in Health Care Industry

Abstract

In the current world, data science has been an emerging technique in all interdisciplinary domains. The statistical analysis provides the prediction over data stimulated from the activities. Using the training dataset, the future predictions have been arrived at and based on the values the model is built. Data mining is working on those statistics and helping the current industries. But Process mining is another important analysis over the process accruing along with data. Process mining techniques use event activities as event log, which relies on the behavior of the events. Process mining will discover all activities, checking compliance, identify bottlenecks and time variants in the flow of process. This is the powerful technique to improve the accuracy in data mining also. This research paper explains the activity event flow in the healthcare industry. The patient’s process flow identified is based on activities log and the deviation time is extracted from the flow of process. The process mining provides the bottleneck activity and this research paper explores the resultant challenges using patient’s events in healthcare systems. Keeping various process mining techniques in view, this research work has been experimented by using two algorithms such as (1) Alpha algorithm and (2) Heuristics Miner algorithm in the healthcare system. This paper deals with the event flow perspective in the Process Mining (ProM) framework to provide the improvement of the existing healthcare system.