| Abstract: | In this study, Decision Trees Algorithms were used
with promising results in various critical problems, concerning
heart sound diagnosis. In general this diagnostic problem can
be divided in many sub problems, each one dealing either with
one morphological characteristic of the heart sound or with
difficult to distinguish heart diseases. The sub problems of the
discrimination of Aortic Stenosis from Mitral Regurgitation
and the discrimination between the second heart sound split,
opening snap and third heart sound, are used as case studies.
Using signalprocessing methods, we extracted the heart sound
feature vector. Relevance analysis was performed using the
Uncertainty Coefficient. Then for each heart sound diagnosis
sub problem, a Specific Decision Tree (DT) was constructed.
Decision Tree pruning was also investigated. Finally, a General
Decision Support System Architecture for the Heart Sound
Diagnosis problem, is proposed. The partial diagnosis, given by
these DT, can be combined using arbitration rules to give the
final diagnosis. These rules can be implemented by another DT,
or can be based on different methods, algorithms, or even on
expert knowledge. All these can lead to an Integrated Decision
Support System Architecture for Heart Sound Diagnosis.
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Greek