Mining healthcare data: the case of an endoscopic thoracic sympathectomy dataset
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
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Primary hyperhidrosis, a disorder characterized by an excessive sweating, has been treated by endoscopic thoracic sympathectomy. As a consequence of the surgery, patients improved their overall quality of life. Their day-by-day activities are not affected, or are less affected, by this disorder, and their emotional state verifies a significant improvement, from a situation of shame and self-punishing to what we could say a normal life. This paper presents the analysis of the quality of life of 227 patients that were treated by an endoscopic thoracic sympathectomy. The study was based on the use of business intelligence technologies, which allowed the storage, the analysis and the reporting of all the relevant findings. In technological terms, this paper illustrates the database and data analysis developments needed in a specific healthcare application domain. For data storage, a data mart was designed addressing the relevant attributes. For data analysis, on-line analytical processing and data mining technologies were used to show the evolution of the patients’, health condition and the incidence of complications or side effects as consequence of the surgery.