Comparative Analysis for Heart Disease Prediction

Sundas Naqeeb Khan - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Nazri Mohd Nawi - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Asim Shahzad - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Arif Ullah - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Muhammad Faheem Mushtaq - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Jamaluddin Mir - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Muhammad Aamir - Universiti Tun Hussein Onn Malaysia, Johor, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.1.4-2.66

Abstract


Today, heart diseases have become one of the leading causes of deaths in nationwide. The best prevention for this disease is to have an early system that can predict the early symptoms which can save more life. Recently research in data mining had gained a lot of attention and had been used in different kind of applications including in medical. The use of data mining techniques can help researchers in predicting the probability of getting heart diseases among susceptible patients. Among prior studies, several researchers articulated their efforts for finding a best possible technique for heart disease prediction model. This study aims to draw a comparison among different algorithms used to predict heart diseases. The results of this paper will helps towards developing an understanding of the recent methodologies used for heart disease prediction models. This paper presents analysis results of significant data mining techniques that can be used in developing highly accurate and efficient prediction model which will help doctors in reducing the number of deaths cause by heart disease.

Keywords


classifiers; heart disease analysis

Full Text:

PDF

References


Purusothaman, G., & Krishnakumari, P. (2015). A survey of data mining techniques on risk prediction: Heart disease. Indian Journal of Science and Technology, 8(12).

Kalaiselvi, C., & Nasira, G. M. (2015). Prediction of heart diseases and cancer in diabetic patients using data mining techniques. Indian Journal of Science and Technology, 8(14).

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

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

Bhatla, N., & Jyoti, K. (2012). An analysis of heart disease prediction using different data mining techniques. International Journal of Engineering, 1(8), 1-4.

Parthiban, G., Rajesh, A., & Srivatsa, S. K. (2011). Diagnosis of heart disease for diabetic patients using naive bayes method. International Journal of Computer Applications, 24(3), 7-11.

Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications, 17(8), 43-48.

Anbarasi, M., Anupriya, E., & Iyengar, N. C. S. N. (2010). Enhanced prediction of heart disease with feature subset selection using genetic algorithm. International Journal of Engineering Science and Technology, 2(10), 5370-5376.

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

Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255

Das, R.; Abdul kadir, S. (2008). “Effective diagnosis of heart disease through neural networks ensemblesâ€. Elsevier, 2008.

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

Palaniappan, S., & Awang, R. (2008, March). Intelligent heart disease prediction system using data mining techniques. In Computer Systems and Applications, 2008.AICCSA 2008. IEEE/ACS International Conference on (pp. 108-115). IEEE.

Patil, S. B., & Kumaras wamy, Y. S. (2009). Intelligent and effective heart attack prediction system using data mining and artificial neural network. European Journal of Scientific Research, 31(4), 642-656.

Lee, H. G., Noh, K. Y., & Ryu, K. H. (2007, May). Mining bio signal data: coronary artery disease diagnosis using linear and nonlinear features of HRV. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 218-228).Springer Berlin Heidelberg.