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Design and Performance Analysis of MLP NN Based Binary Classifier for Heart Diseases


Affiliations
1 P. G. Department of Applied Electronics, SGB Amravati University, Amravati- 444602 (M.S.), India
 

Experiments with the Switzerland heart disease database have concentrated on attempting to distinguish presence and absence. The classifiers based on various neural networks, namely, MLP, PCA, Jordan, GFF, Modular, RBF, SOFM, SVM NNs and conventional statistical techniques such as DA and CART are optimally designed, thoroughly examined and performance measures are compared in this study. With chosen optimal parameters of MLP NN, when it is trained and tested over cross validation (unseen data sets), the average (and best respectively) classification of 98±2.83 % (and 100%), 96.67±4.56% overall accuracy, sensitivity 96±5.48, specificity 100% are achieved which shows consistent performance than other NN and statistical models. The results obtained in this work show the potentiality of the MLP NN approach for heart diseases classification.

Keywords

Heart Disease, MLP Neural Network, Error Back Propagation Algorithm, Performance
User

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  • Design and Performance Analysis of MLP NN Based Binary Classifier for Heart Diseases

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Authors

Ranjana Raut
P. G. Department of Applied Electronics, SGB Amravati University, Amravati- 444602 (M.S.), India
S. V. Dudul
P. G. Department of Applied Electronics, SGB Amravati University, Amravati- 444602 (M.S.), India

Abstract


Experiments with the Switzerland heart disease database have concentrated on attempting to distinguish presence and absence. The classifiers based on various neural networks, namely, MLP, PCA, Jordan, GFF, Modular, RBF, SOFM, SVM NNs and conventional statistical techniques such as DA and CART are optimally designed, thoroughly examined and performance measures are compared in this study. With chosen optimal parameters of MLP NN, when it is trained and tested over cross validation (unseen data sets), the average (and best respectively) classification of 98±2.83 % (and 100%), 96.67±4.56% overall accuracy, sensitivity 96±5.48, specificity 100% are achieved which shows consistent performance than other NN and statistical models. The results obtained in this work show the potentiality of the MLP NN approach for heart diseases classification.

Keywords


Heart Disease, MLP Neural Network, Error Back Propagation Algorithm, Performance

References





DOI: https://doi.org/10.17485/ijst%2F2009%2Fv2i8%2F29508