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Analysis of Predictive Models for Cardiovascular Heart Disease Diagnosis

Sunila Godara, Dr. Prabhat Panday


Medical science industry has huge amount of data, but most of this data is not mined to find out hidden information in data. Data mining techniques can be used to discover hidden patterns. Diagnosing of heart disease is one of important issue to develop medical decision support system which will help the physicians to take effective decision. In this research paper data mining classification techniques Random Decision Tree , Decision Tree Forest, Artificial neural networks (ANNs), and Support Vector Machine (SVM) are analyzed on cardiovascular disease dataset. Performance of these techniques is compared through sensitivity, specificity, accuracy, F measure, True Positive Rate, False Positive Rate and ROC. In our study 10-fold cross validation method was used to measure the unbiased estimate of these prediction models.


Heart Disease, Data Mining Techniques, Random Decision Tree, Decision Tree Forest, Artificial Neural Networks, and Support Vector Machine

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Yanwei, X.; Wang, J.; Zhao, Z.; and Gao, Y. (2007). “Combination data mining models with new medical data to predict outcome of coronary heart disease”. Proceedings International Conference on Convergence Information Technology 2007, p 868 – 872.

Khemphila, A.; Boonjing, V. (2010). “Comparing performance of logistic regression, decision trees and neural networks for classifying heart disease patients”. Proceedings of International Conference on Computer Information System and Industrial Management Applications 2010, p 193 – 198.

Detrano, R.; Steinbrunn, W.; Pfisterer, M. (1987). “International application of a new probability algorithm for the diagnosis of coronary artery disease”. American Journal of Cardiology, Vol. 64, No. 3, 1987, p 304-310.

Yao, Z.; Lei, L.; Yin, J. (2005). “R-C4.5 Decision tree model and its applications to health care dataset”. Proceedings of International Conference on Services Systems and Services Management 2005, p 1099-1103.

Das, R.; Abdulkadir, S. (2008). “Effective diagnosis of heart disease through neural networks ensembles”. Elsevier, 2008.

Colombet, I.; Ruelland, A.; Chatellier, G.; Gueyffier, F. (2000). “Models to predict cardiovascular risk: comparison of CART, multilayer perceptron and logistic regression”. Proceedings of AMIA Symp 2000, p 156-160.

Avci, E.; Turkoglu, I. (2009). “An intelligent diagnosis system based on principle component analysis and ANFIS for the heart valve diseases”. Journal of Expert Systems with Application, Vol. 2, No. 1, 2009, p 2873-2878.

Kurt, I.; Ture, M.; Turhan, A. (2008). “Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease”. Journal of Expert Systems with Application, Vol. 3, 2008, p 366-374.

Gennari, J. (1989). “Models of incremental concept formation”. Journal of Artificial Intelligence, Vol. 1, 1989, p 11-61.

Cohen, W. (1995). “Fast effective rule induction”. Proceedings of International Conference on machine Learning 1995, p 1-10.

Chau, M.; Shin, D. (2009). “A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms”. Proceedings of IEEE International Conference on Dependable, Autonomic and Secure Computing 2009, p 183-187.

Patil, S.; Kumaraswamy, Y. (2009). “Intelligent and effective Heart Attack prediction system using data mining and artificial neural networks”. European Journal of Scientific Research, Vol. 31, 2009, p 642- 656.

Han, J.; Kamber, M. (2006). “Data Mining Concepts and Techniques”. 2nd Edition, Morgan Kaufmann, San Francisco.

Palaniappan, S.; Awang, R. (2008). “Intelligent Heart Disease Prediction System Using Data Mining Techniques”. Proceedings of IEEE/ACS International Conference on Computer Systems and Applications 2008, p 108-115.


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