Performance evaluation of different machine learning techniques for prediction of heart disease
- 273 Downloads
Heart diseases are of notable public health disquiet worldwide. Heart patients are growing speedily owing to deficient health awareness and bad consumption lifestyles. Therefore, it is essential to have a framework that can effectually recognize the prevalence of heart disease in thousands of samples instantaneously. At this juncture, the potential of six machine learning techniques was evaluated for prediction of heart disease. The recital of these methods was assessed on eight diverse classification performance indices. In addition, these methods were assessed on receiver operative characteristic curve. The highest classification accuracy of 85 % was reported using logistic regression with sensitivity and specificity of 89 and 81 %, respectively.
KeywordsMachine learning Classification Heart disease Treatments Artificial neural network Support vector machine
I am greatly thankful to Department of Biotechnology, New Delhi, for providing Bioinformatics Infrastructure Facility of DBT at Maulana Azad National Institute of Technology, Bhopal.
Compliance with ethical standards
Conflict of interest
- 2.Heydari M, Teimouri M, Heshmati Z, Alavinia SM (2015) Comparison of various classification algorithms in the diagnosis of type 2 diabetes in Iran. Int J Diabetes Dev Ctries 36(2):1–7Google Scholar
- 4.Kalaiselvi C, Nasira GM (2015) Classification and prediction of heart disease from diabetes patients using hybrid particle swarm optimization and library support vector machine algorithm. Int J Comput Algorithm 4(1):2278–2397Google Scholar
- 6.King RD (1992) Statlog databases. Department of Statistics and Modelling Science, University of Strathclyde, GlasgowGoogle Scholar
- 7.Bache K, Lichman M (2013) UCI machine learning repository [http://archive.ics.uci.edu/ml]. University of California, School of Information and Computer Science. Irvine, CA
- 9.Yao X, Liu Y (1998) Making use of population information in evolutionary artificial neural networks. Syst Man Cybern Part B: Cybern IEEE Trans 28(3):417–425Google Scholar
- 24.Jensen FV (1996) An introduction to Bayesian networks, vol 210. UCL press, LondonGoogle Scholar
- 25.Peral J (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Mateo, Cali fornia 12:241–288Google Scholar
- 34.Metz CE (1978) Basic principles of ROC analysis. In: Freeman LM (ed) Seminars in nuclear medicine. vol 4. Elsevier, pp 283–298Google Scholar