Performance Analysis and Activity Deviation Discovery in Event Log Using Process Mining Tool for Hospital System
محورهای موضوعی : Machine learning
Shanmuga Sundari M
1
(Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation )
Rudra Kalyan Nayak
2
(School of Computing Science and Engineering, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, Madhya Pradesh - 466114)
Vijaya Chandra Jadala
3
( Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation)
Sai Kiran Pasupuleti
4
(Department of CSE, Prasad V Potluri Siddhartha Institute of Technology)
کلید واژه: Alpha Miner, Event log, Fuzzy Miner, Hospital Process, Process Mining,
چکیده مقاله :
All service and manufacturing businesses are resilient and strive for a more efficient and better end in today's world. Data mining is data-driven and necessitates significant data to analyze the pattern and train the model. Assume the data is incorrect and was not collected from reliable sources, causing the analysis to be skewed. We introduce a procedure in which the dataset is split into test and training datasets with a specific ratio to overcome this challenge. Process mining will find the traces of actions to streamline the process and aid data mining in producing a more efficient result. The most responsible domain is the healthcare industry. In this study, we used the activity data from the hospital and applied process mining algorithms such as alpha miner and fuzzy miner. Process mining is used to check for conformity in the event log and do performance analysis, and a pattern of accuracy is exhibited. Finally, we used process mining techniques to show the deviation flow and fix the process flow. This study showed that there was a variation in the flow by employing alpha and fuzzy miners in the hospital.
All service and manufacturing businesses are resilient and strive for a more efficient and better end in today's world. Data mining is data-driven and necessitates significant data to analyze the pattern and train the model. Assume the data is incorrect and was not collected from reliable sources, causing the analysis to be skewed. We introduce a procedure in which the dataset is split into test and training datasets with a specific ratio to overcome this challenge. Process mining will find the traces of actions to streamline the process and aid data mining in producing a more efficient result. The most responsible domain is the healthcare industry. In this study, we used the activity data from the hospital and applied process mining algorithms such as alpha miner and fuzzy miner. Process mining is used to check for conformity in the event log and do performance analysis, and a pattern of accuracy is exhibited. Finally, we used process mining techniques to show the deviation flow and fix the process flow. This study showed that there was a variation in the flow by employing alpha and fuzzy miners in the hospital.
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