| Abstract: | The development of ‘intelligent’ medical
equipment, which can not only acquire various signals from the
human body, but also process them and provide
recommendations as to probable pathological conditions, will
be highly beneficial for both the medical personnel and the
patients. However, this necessitates the development and
exploitation of advanced highly efficient classification
techniques. In this direction this paper presents a novel
ensemble classification technique, combining Random Forests
with the ‘Markov Blanket’ notion, which is used for the
automated diagnosis of aortic and mitral heart valves diseases
from low-cost and easily acquired heart sound signals. It has
been tested in a highly ‘difficult’ global and heterogeneous
dataset of 198 heart sound signals, which been acquired from
both healthy and pathological medical cases. The proposed
ensemble classification technique exhibited a higher
classification performance in comparison with the classical
Random Forest algorithms, and also other widely used
classification algorithms. |
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