Automated Aortic and Mitral Valves Diseases Diagnosis from Heart Sound Signals Using Novel Ensemble Classification Techniques

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.