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- W2188445793 abstract "is making tremendous strides and as such various guidelines and position papers are regularly being published still competency and knowledge updating of all concerned is key requirement. In face of uncertainty of disease symptoms even experienced cardiologists need complimentary assistance from intelligent decision system to arrive at precise diagnosis of cardiac diseases. This research work is aimed at developing it using neural network approach. This study sought to assess the usefulness and accuracy of artificial neural networks (ANN) in the Echocardiographic view classification of the patient's survival and death in patients with heart failure. Paper discusses performances of self organizing feature map (SOFM) and Jordan neural network (JORDAN NN) approaches for classification on two different data sets (unseen data). SOFM NN approach gives the best results over JORDAN NN, and outperforms in various performance measures, such as accuracy (100% correct detection, only 0.92 % error in multifold cross validation). Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed." @default.
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- W2188445793 date "2009-01-01" @default.
- W2188445793 modified "2023-09-22" @default.
- W2188445793 title "Determination Of Long-Term Outcome In Patients With Coronery Artery Disease Using An Artificial Neural Network" @default.
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