Rakel Model For Multi Class Label Classification Using Ensemble Neural PCA On Healthcare Event Log
DOI:
https://doi.org/10.32628/CSEIT228646Keywords:
Process Mining, PCA, Neural, Ensemble, treatment, Health care, Cancer Data.Abstract
Process change over time is of particular issue in the field of healthcare, as healthcare practices emerge and change in response to the individual needs of patients. We propose a systematic procedure to study the change in process in time, which is appropriate for the complex field of healthcare. Our approach is based on qualitative process comparison that is based on 3 levels: A broad viewpoint (process model) and a mid-level perspective (trace) and a fine-grained, detailed (activity). Our goal was to identify the changes, and understand the process's evolution. We demonstrate this approach by through a case study of tumor pathways within Leeds where we observed evidence of change points at various levels. This paper will expand our investigation by using redundancy strategies employing Neural PCA. We labeling the labels in order to determine and analyzing the miners utilized in process discovery. We also provide an in-depth analysis of the process of research at the trace and activity levels using group classifiers. Through our study we demonstrate that this approach is qualitative and can provide a valuable understanding of changes in process in time. Analyzing change on three levels will provide evidence for the process's evolution when different perspectives agree and contradictory evidence may result in a discussion with experts in the field. This approach is useful to those who are dealing with complex processes that undergo changes in time.
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