Abstract
Nowadays, since any area has large amounts of data (Big Data), the underlying value of this data can be of high importance. These data represent an opportunity to obtain information to improve the institutions operating. The health area is not different and, as everyone knows, any issue related to this area is always sensitive, due to the importance it has to the general population.
This work intends to analyze some Process Mining algorithms, namely in process discovery, using data of healthcare area. Different tools were used and, for each one, it is possible to understand which algorithm leads to better results.
The results showed that the Disco is the simplest and most intuitive tool for discovering processes, with an automatic but very effective implementation of an improved and proven version of the Fuzzy Miner algorithm. Conversely, PM4Py is a framework that allows great algorithmic customization, being ideal for professionals with knowledge in the area. Inductive Miner was the algorithm which achieved better results, since this algorithm has been improved in the search for divisions/patterns in the logs. This same algorithm was the one that leaded the best results in the ProM tool. However, this is the tool less intuitive, with modules that can require a higher level of knowledge do deal with them.
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Acknowledgements
This work is funded by National Funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project Ref UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD), the Polytechnic of Viseu for their support.
This work is also funded by National Funds through the FCT - Foundation for Science and Technology, I.P., within the scope of the project Refª UIDB/05507/2020. Furthermore, we would like to thank the Centre for Studies in Education and Innovation (CI&DEI) and the Polytechnic of Viseu for their support.
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Gomes, A.F.D., de Lacerda, A.C.W.G., da Silva Fialho, J.R. (2022). Comparative Analysis of Process Mining Algorithms in Process Discover. In: de Paz Santana, J.F., de la Iglesia, D.H., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2021. Advances in Intelligent Systems and Computing, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-87687-6_25
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