NDLI: Cardiac disorders detection approach based on local transfer function classifier

Content Provider	IEEE Xplore Digital Library
Author	Hamdy, A. El-Bendary, N. Khodeir, A. Fouad, M.M.M. Hassanien, A.E. Hefny, H.
Copyright Year	2013
Description	Author affiliation: Fac. of Sci., Ain Shams Univ., Cairo, Egypt (Khodeir, A.) \|\| Fac. of Comput. & Inf., Cairo Univ., Cairo, Egypt (Hassanien, A.E.) \|\| Arab Acad. for Sci., Technol. & Maritime Transp., Cairo, Egypt (El-Bendary, N.; Fouad, M.M.M.) \|\| Dept. of Comput. Sci. & Inf., Cairo Univ., Cairo, Egypt (Hamdy, A.; Hefny, H.)
Abstract	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 his performance 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 researchers with abundance of sound signals that was classified using different classification algorithms; decision tree, rotation forest, random forest are few to mention. This paper proposes an approach based on local transfer function classifier as a new model of neural networks for heart valve diseases detection. In order to achieve this objective, and to increase the efficiency of the predication model, boolean reasoning discretization algorithm is introduced to discretion 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. The experimental results obtained, show that the overall accuracy offered by the employed local transfer function classifier was high compared with other techniques including decision table, r
Starting Page	55
Ending Page	61
File Size	531563
Page Count	7
File Format	PDF
ISBN	9788360810521
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-08
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Polish Information Processing Society
Subject Keyword	Heart Accuracy Feature selection LTF-C Transfer functions Rough sets Machine learning Feature extraction Valves Diseases Cardiac disorders
Content Type	Text
Resource Type	Article

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