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Paper Title
Hybrid SVM-ANN Classifier is used for Heart Disease Prediction System
Authors
  S.Shylaja,  R. Muralidharan

Abstract
Heart disease is one of the reason behind death of people universally, more people pass away from this disease compare to any other cause annually. To stay away from heart disease or find symptoms early, adults over 40 plus should have a complete cardiovascular checkup. Many experts developing intelligent decision support systems related to medical to get better ability of the detection of heart disease. In heart disease diagnosis and treatment, hybrid data mining techniques provide the reasonable accuracy level compare to other existing techniques. A hybrid classifier obtained by hybridization of Support Vector Machine and Artificial Neural Network classifier. In this proposed method a predictive analysis is carried out on UCI Heart disease dataset using SVM and ANN techniques. This SVM-ANN hybrid classifier performance much better than standard version of support vector machine and artificial neural networks. The obtained accuracy of this technique is 88.54%. This result shows that SVM-ANN is the best hybrid algorithm for diagnosis of heart disease

Keywords- Data Mining, Support Vector Machine, Artificial Neural Network, Hybrid SVM-ANN
Publication Details
Unique Identification Number - IJEDR1903062
Page Number(s) - 365-372
Pubished in - Volume 7 | Issue 3 | August 2019
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  S.Shylaja,  R. Muralidharan,   "Hybrid SVM-ANN Classifier is used for Heart Disease Prediction System", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.7, Issue 3, pp.365-372, August 2019, Available at :http://www.ijedr.org/papers/IJEDR1903062.pdf
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