title: Cardiovascular Dysautonomias Diagnosis Using Crisp and Fuzzy Decision Tree: A Comparative Study.
abstract: Decision trees (DTs) are one of the most popular techniques for learning classification systems, especially when it comes to learning from discrete examples. In real world, many data occurred in a fuzzy form. Hence a DT must be able to deal with such fuzzy data. In fact, integrating fuzzy logic when dealing with imprecise and uncertain data allows reducing uncertainty and providing the ability to model fine knowledge details. In this paper, a fuzzy decision tree (FDT) algorithm was applied on a dataset extracted from the ANS (Autonomic Nervous System) unit of the Moroccan university hospital Avicenne. This unit is specialized on performing several dynamic tests to diagnose patients with autonomic disorder and suggest them the appropriate treatment. A set of fuzzy classifiers were generated using FID 3.4. The error rates of the generated FDTs were calculated to measure their performances. Moreover, a comparison between the error rates obtained using crisp and FDTs was carried out and has proved that the results of FDTs were better than those obtained using crisp DTs.
authors.affiliation: Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco.
authors.forename: Ilham
authors.initials: I
authors.lastname: Kadi
authors.affiliation: Software Project Management Research Team, ENSIAS, Mohammed V University in Rabat, Morocco.
authors.forename: Ali
authors.initials: A
authors.lastname: Idri
mesh_terms: D000465:Algorithms; D001341:Autonomic Nervous System / Q000503:physiopathology; D001794:Blood Pressure / Q000502:physiology; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000503:physiopathology; D003661:Decision Support Techniques; D003663:Decision Trees; D017143:Fuzzy Logic; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D054969:Primary Dysautonomias / Q000175:diagnosis* / Q000503:physiopathology