Rough local transfer function for cardiac disorders detection using heart sounds

Citation:
Hassanien, A. E., M. Salama, J. Platos, and V. Snasel, "Rough local transfer function for cardiac disorders detection using heart sounds ", Logic Journal of the IGPL, Oxford, vol. (in press), 2014.

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Truly, heart is successor to the brain in being the most significant vital organ

in the body of a human. Heart, being a magnificent pump, has its perfor-
mance orchestrated via a group of valves and highly sophisticated neural

control. While the kinetics of the heart is accompanied by sound production,

sound waves produced, by the heart, are reliable diagnostic tools to check

heart activity. Chronologically, several data sets have been put forward to

sneak on the heart performance and lead to medical intervention whenever

necessary. The heart sounds data set, utilized in this paper, provides re-
searchers with abundance of sound signals that was classified using different

classification algorithms; neural network, rotation forest, and random forest

are few to mention. This paper proposes an approach based on rough sets

and local transfer function classifier for heart valve diseases detection. In

order to achieve this objective, and to increase the efficiency of the pred-
ication model, boolean reasoning discretization algorithm is introduced to

discrete the heart signal data set, then the rough set reduction technique is

applied to find all reducts of the data which contains the minimal subset of

attributes that are associated with a class label for classification. Then, the

rough sets dependency rules are generated directly from all generated reducts.

Rough confusion matrix is used to evaluate the performance of the predicted

reducts and classes. Finally, a local transfer function classifier was employed

to evaluate the ability of the selected descriptors for discrimination whether

they represent healthy or unhealthy. Alternative classifiers have been applied 

plied to the same data for comparison including Support Vector Machine

(SVM), Hidden Naive Bayesian Network (HNB), Bayesian Network (BN),

Naive Bayesian Tree (NBT), Decision Tree (DT), Sequential Minimal Opti-
mization (SMO), Decision Table (DT), Rotation Forest (RoF), and Random

Forest (RF), however their performance for the same diagnostic problems

was lower than the proposed rough local transfer function.

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