Timely diagnosis of the heart disease using data mining algorithm

Hamid Reza Sahebi; Mohammad Azimimehr

Timely diagnosis of the heart disease using data mining algorithm

Hamid Reza Sahebi, Mohammad Azimimehr

Abstract

In the current state of medical knowledge, we are facing abundant data collection about various diseases. Investigating these data and obtaining useful results and patterns in conjunction with diseases, is the main propose for using them. In this paper, considering the significant share of the heart disease in human mortality, a variety of data mining methods and algorithms are applied for timely diagnosis of heart diseases. For this purpose, Dezful Hospital standardized data collection for patients with heart disease, have been used. Most important variables of this collection are exercise-induced angina, type of chest pain, age, maximum heart rate and blood pressure at rest. Classification algorithms of K-NN, PSO, Bagging and SVM + PSO hybrid algorithm, are tested and evaluated on the data sets. Based on our evaluations, the proposed hybrid algorithm, with accuracy of 94.74, had the highest accuracy.

Keywords

Classification algorithms; K-NN; Bagging; SVM-PSO hybrid algorithm

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References

A. Khasseh, "data mining, text mining, and web mining: definitions and applications.," 2009.

R. C. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Proceedings of the sixth international symposium on micro machine and human science, 1995, pp. 39-43.

M. Pant, R. Thangaraj, and A. Abraham, "Particle swarm optimization: performance tuning and empirical analysis," in Foundations of Computational Intelligence Volume 3, ed: Springer, 2009, pp. 101-128.

L. Breiman, "Bagging predictors," Technical Report 421, Department of Statistics, University of California at Berkeley, 1994.

K. Machová, F. Barcak, and P. Bednár, "A bagging method using decision trees in the role of base classifiers," Acta Polytechnica Hungarica, vol. 3, pp. 121-132, 2006.

N. Bhatia, "Survey of nearest neighbor techniques," arXiv preprint arXiv:1007.0085, 2010.

B. Deekshatulu and P. Chandra, "Classification of heart disease using K-nearest neighbor and genetic algorithm," Procedia Technology, vol. 10, pp. 85-94, 2013.

V. Vapnik, The nature of statistical learning theory: Springer Science & Business Media, 2013.

H. Yamada and Y. Matsumoto, "Statistical dependency analysis with support vector machines," in Proceedings of IWPT, 2003, pp. 195-206.

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