Decision tree induction: a useful tool for assisted diagnosis and prognosis in tumor pathology
Decaestecker, C.; Camby, I.; Remmelink, M.; Nagy, N.; Petein, M.; Pasteels, J.L.; Van Ham, P.; Salmon, I.; Kiss, R.
Laboratory Investigation; A Journal of Technical Methods and Pathology 76(6): 799-808
1997
ISSN/ISBN: 0023-6837 PMID: 9194856 Document Number: 470825
The aim of the present work is to show that decision tree induction algorithms are a useful tool for extracting reliable information from data series, with the objective of assisting pathologists in identifying specific diagnostic and prognostic markers in various types of tumor pathologies. In terms of accuracy, we show that the decision tree technique exceeds other more sophisticated techniques, such as multilayer neural networks. Furthermore, because of the ease with which decision tree results can be interpreted (logical classification rules), new methodologies can be readily developed to further assist in analyzing complex data that mix heterogeneous features. In this paper, we illustrate such capabilities in the context of different complex diagnostic and/or prognostic problems in tumor pathology relating to bladder, astrocytomas, and adipose tissues.