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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Inducing Decision Trees from Medical Decision Processes
verfasst von : Pere Torres, David Riaño, Joan Albert López-Vallverdú
Erschienen in: Knowledge Representation for Health-Care
Verlag: Springer Berlin Heidelberg
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In medicine, decision processes are correct not only if they conclude with a right final decision, but also if the sequence of observations that drive the whole process to the final decision defines a sequence with a medical sense. Decision trees are formal structures that have been successfully applied to make decisions in medicine; however, the traditional machine learning algorithms used to induce these trees use information gain or cost ratios that cannot guarantee that the sequences of observations described by the induced trees have a medical sense. Here, we propose a slight variation of classical decision tree structures, provide four quality ratios to measure the medical correctness of a decision tree, and introduce a machine learning algorithm to induce medical decision trees whose final decisions are both correct and the result of a sequence of observations with a medical sense. The algorithm has been tested with four medical decision problems, and the successful results discussed.