SQL based cardiovascular ultrasound image classification
by S. Nandagopalan; Adiga B. Suryanarayana; T.S.B. Sudarshan; Dhanalakshmi Chandrashekar; C.N. Manjunath
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 7, No. 3, 2013

Abstract: This paper proposes a novel method to analyze and classify the cardiovascular ultrasound echocardiographic images using Naïve-Bayesian model via database OLAP-SQL. Efficient data mining algorithms based on tightly-coupled model is used to extract features. Three algorithms are proposed for classification namely Naïve-Bayesian Classifier for Discrete variables (NBCD) with SQL, NBCD with OLAP-SQL, and Naïve-Bayesian Classifier for Continuous variables (NBCC) using OLAP-SQL. The proposed model is trained with 207 patient images containing normal and abnormal categories. Out of the three proposed algorithms, a high classification accuracy of 96.59% was achieved from NBCC which is better than the earlier methods.

Online publication date: Wed, 12-Jun-2013

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