Boosted decision trees for diagnosis type of hypertension

Authors:
Michal Wozniak
Affiliations:
Chair of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland
Venue:
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Year:
2005

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Abstract

The inductive learning algorithms are the very attractive methods generating hierarchical classifiers. They generate hypothesis of the target concept on the base on the set of labeled examples. This paper presents some of the decision tree induction methods, boosting concept and their usefulness for diagnosis of the type of hypertension (essential hypertension and five type of secondary one: fibroplastic renal artery stenosis, atheromatous renal artery stenosis, Conn's syndrome, renal cystic disease and pheochromocystoma). The decision on the type of hypertension is made only on base on blood pressure, general information and basis biochemical data.