Mining Data from a Knowledge Management Perspective: An Application to Outcome Prediction in Patients with Resectable Hepatocellular Carcinoma
This paper presents the use of data mining tools to derive a prognostic model of the outcome of resectable hepatocellular carcinoma. The main goal of the study was to summarize the experience gained over more than 20 years by a surgical team. To this end, two decision trees have been induced from data: a model M1 that contains a full set of prognostic rules derived from the data on the basis of the 20 available factors, and a model M2 that considers only the two most relevant factors. M1 will be used to explicit the knowledge embedded in the data (externalization), while the model M2 will be used to extract operational rules (socialization). The models performance has been compared with the one of a Naive Bayes classifier and have been validated by the expert physicians. The paper concludes that a knowledge management perspective improves the validity of data mining techniques in presence of small data sets, coming from severe pathologies with relative low incidence. In these cases, it is more crucial the quality of the extracted knowledge than the predictive accuracy gained.
KeywordsLiver Resection Prognostic Model Total Accuracy Decision Tree Induction Intelligent Data Analysis
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