Mining healthcare data: the case of an endoscopic thoracic sympathectomy dataset

Authors:
Maribel Yasmina Santos;Diana Gonçalves;Jorge Cruz
Affiliations:
Algoritmi Research Centre, University of Minho, Guimarães, Portugal;Algoritmi Research Centre, University of Minho, Guimarães, Portugal;Medicine Faculty, University of Lisbon, Lisboa, Portugal
Venue:
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Year:
2010

Citing 4
Cited 0

Data mining and knowledge discovery in databases

Communications of the ACM
Advances in knowledge discovery and data mining

Advances in knowledge discovery and data mining
Data mining: concepts and techniques

Data mining: concepts and techniques
Analysis of the Quality of Life after an Endoscopic Thoracic Sympathectomy: A Business Intelligence Approach

DBKDA '10 Proceedings of the 2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications

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Abstract

The process of knowledge discovery in databases aims at the discovery of associations within data in a dataset. Data Mining is a central step of this process corresponding to the application of algorithms for identifying patterns in data. This paper presents the particular case of analysis of a dataset containing data associated with 227 patients submitted to an endoscopic thoracic sympathectomy, a treatment for primary palmar hyperhidrosis. Primary hyperhidrosis is characterized by an excessive sweating that appears as a consequence of a disorder of the sympathetic autonomous nervous system. The results achieved show an overall improvement of the patients' quality of life, mainly associated with their emotional state.