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01-08-2007

Predicting Metastasis in Breast Cancer: Comparing a Decision Tree with Domain Experts

Authors: Amir R. Razavi, Hans Gill, Hans Åhlfeldt, Nosrat Shahsavar

Published in: Journal of Medical Systems | Issue 4/2007

Abstract

Breast malignancy is the second most common cause of cancer death among women in Western countries. Identifying high-risk patients is vital in order to provide them with specialized treatment. In some situations, such as when access to experienced oncologists is not possible, decision support methods can be helpful in predicting the recurrence of cancer. Three thousand six hundred ninety-nine breast cancer patients admitted in south-east Sweden from 1986 to 1995 were studied. A decision tree was trained with all patients except for 100 cases and tested with those 100 cases. Two domain experts were asked for their opinions about the probability of recurrence of a certain outcome for these 100 patients. ROC curves, area under the ROC curves, and calibration for predictions were computed and compared. After comparing the predictions from a model built by data mining with predictions made by two domain experts, no significant differences were noted. In situations where experienced oncologists are not available, predictive models created with data mining techniques can be used to support physicians in decision making with acceptable accuracy.

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Title: Predicting Metastasis in Breast Cancer: Comparing a Decision Tree with Domain Experts
Authors: Amir R. Razavi
Hans Gill
Hans Åhlfeldt
Nosrat Shahsavar
Publication date: 01-08-2007
Publisher: Kluwer Academic Publishers-Plenum Publishers
Published in: Journal of Medical Systems / Issue 4/2007
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-007-9064-1

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Predicting Metastasis in Breast Cancer: Comparing a Decision Tree with Domain Experts

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