References

Ahmad, M.A.B., 2013. Mining health data for breast cancer diagnosis using machine learning. Ph.D Thesis, University of Canberra, Canberra, Australia.

Alhaj, T.A., M.M. Siraj, A. Zainal, H.T. Elshoush and F. Elhaj, 2016. Feature selection using information gain for improved structural-based alert correlation. PloS One, 11: 1-18.
CrossRef  |  PubMed  |  Direct Link  |  

Andreeva, P., 2006. Data modelling and specific rule generation via data mining techniques. Proceedings of the International Conference on Computer Systems and Technologies-CompSysTech, June 15-16, 2006, University of Ruse, Ruse, Bulgaria, pp: IIIA.17-1-IIIA.17-6.

Ashraf, M., G. Chetty, D. Tran and D. Sharma, 2012. Hybrid approach for diagnosing thyroid, hepatitis and breast cancer based on correlation based feature selection and Naive bayes. Proceedings of the International Conference on Neural Information Processing, November 12-15, 2012, Springer, Berlin, Germany, ISBN:978-3-642-34477-0, pp: 272-280.

Ashraf, M., K. Le and X. Huang, 2010. Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses. Proceedings of the 2010 5th International Conference on Computer Sciences and Convergence Information Technology (ICCIT), November 30-December 2, 2010, IEEE, Seoul, South Korea, ISBN:978-1-4244-8567-3, pp: 911-915.

Ashraf, M., K. Le and X. Huang, 2011. Iterative weighted k-NN for constructing missing feature values in Wisconsin breast cancer dataset. Proceedings of the 2011 3rd International Conference on Data Mining and Intelligent Information Technology Applications (ICMiA), October 24-26, 2011, IEEE, Macao, China, ISBN:978-1-4673-0231-9, pp: 23-27.

Breve, F.A. and D.C.G. Pedronette, 2016. Combined unsupervised and semi-supervised learning for data classification. Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), September 13-16, 2016, IEEE, Vietri sul Mare, Italy, ISBN:978-1-5090-0747-9, pp: 1-6.

Canedo, V.B., N.S. Marono, A.A. Betanzos, J.M. Benitez and F. Herrera, 2014. A review of microarray datasets and applied feature selection methods. Inf. Sci., 282: 111-135.
CrossRef  |  Direct Link  |  

Cheung, N., 2001. Machine learning techniques for medical analysis. BSc Thesis, School of Information Technology and Electrical Engineering, University of Queenland, Brisbane, Australia.

Chuang, L.Y., C.H. Ke and C.H. Yang, 2008. A hybrid both filter and wrapper feature selection method for microarray classification. Proceedings of the International MultiConference on Engineers and Computer Scientists (IMECS’2008) Vol. 1, March 19-21, 2008, International Association of Engineers, Hong Kong, ISBN:978-988-98671-8-8, pp: 1-5.

Demidova, L., Y. Sokolova and E. Nikulchev, 2015. Use of fuzzy clustering algorithms ensemble for SVM classifier development. Intl. Rev. Mod. Simul., 8: 446-457.
CrossRef  |  Direct Link  |  

Dimitoglou, G., J.A. Adams and C.M. Jim, 2012. Comparison of the C4.5 and a Naive Bayes classifier for the prediction of lung cancer survivability. J. Comput., 4: 1-9.
Direct Link  |  

Dua, S. and X. Du, 2016. Data Mining and Machine Learning in Cybersecurity. CRC Press, Boca Raton, Florida, USA., ISBN-13:978-1-4398-3943-0, Pages: 224.

Fernandez-Delgado, M., E. Cernadas, S. Barro and D. Amorim, 2014. Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res., 15: 3133-3181.
Direct Link  |  

Fowkes, F.G.R., D. Rudan, I. Rudan, V. Aboyans and J.O. Denenberg et al., 2013. Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: A systematic review and analysis. Lancet, 382: 1329-1340.
CrossRef  |  PubMed  |  Direct Link  |  

Frank, E., M. Hall, L. Trigg, G. Holmes and I.H. Witten, 2004. Data mining in bioinformatics using Weka. Bioinformatics, 20: 2479-2481.
CrossRef  |  

Inbarani, H.H., M. Bagyamathi and A.T. Azar, 2015. A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput. Appl., 26: 1859-1880.
CrossRef  |  Direct Link  |  

Kangwanariyakul, Y., T. Naenna, C. Nantasenamat and T. Tantimongcolwat, 2010. Data mining of magnetocardiograms for prediction of ischemic heart disease. Excli J., 9: 82-95.
Direct Link  |  

Karegowda, A.G., A.S. Manjunath and M.A. Jayaram, 2010. Comparative study of attribute selection using gain ratio and correlation based feature selection. Int. J. Inform. Technol. Knowledge Manage., 2: 271-277.
Direct Link  |  

Kavitha, R. and E. Kannan, 2016. An efficient framework for heart disease classification using feature extraction and feature selection technique in data mining. Proceedings of the 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), February 24-26, 2016, IEEE, Pudukkottai, India, ISBN:978-1-4673-6726-4, pp: 1-5.

Kinge, D. and S.K. Gaikwad, 2018. Survey on data mining techniques for disease prediction. Intl. Res. J. Eng. Technol., 5: 630-636.
Direct Link  |  

Kotsiantis, S.B., 2007. Supervised machine learning: A review of classification techniques. Informatica, 31: 249-268.
Direct Link  |  

Krishnaiah, V., G. Narsimha and N.S. Chandra, 2014. Survey of classification techniques in data mining. Intl. J. Comput. Sci. Eng., 2: 65-74.
Direct Link  |  

Kumar, A., S. Roy and P. Ranjan, 2014. Dimensionality reduction for high dimensional data: A CFS and Chi Square approach. Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies, November 14-16, 2014, ACM, New York, USA., ISBN:978-1-4503-3216-3, pp: 1-6.

Long, N.C., P. Meesad and H. Unger, 2015. A highly accurate firefly based algorithm for heart disease prediction. Expert Syst. Appl., 42: 8221-8231.
Direct Link  |  

Pal, M. and P.M. Mather, 2005. Support vector machines for classification in remote sensing. Intl. J. Remote Sens., 26: 1007-1011.
CrossRef  |  Direct Link  |  

Pouriyeh, S., S Vahid, G. Sannino, G. De Pietro and H. Arabnia et al., 2017. A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease. Proceedings of the 2017 IEEE International Symposium on Computers and Communications (ISCC), July 3-6, 2017, IEEE, Heraklion, Greece, ISBN:978-1-5386-1630-7, pp: 204-207.

Saravanan, N., V.N.S.K. Siddabattuni and K.I. Ramachandran, 2008. A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box. Exp. Syst. Appl., 35: 1351-1366.
CrossRef  |  Direct Link  |  

Shao, Y.E., C.D. Hou and C.C. Chiu, 2014. Hybrid intelligent modeling schemes for heart disease classification. Appl. Soft Comput., 14: 47-52.
CrossRef  |  Direct Link  |  

Singh, A., N. Thakur and A. Sharma, 2016. A review of supervised machine learning algorithms. Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), March 16-18, 2016, IEEE, New Delhi, India, ISBN:978-1-4673-9417-8, pp: 1310-1315.

Wald, R., T.M. Khoshgoftaar and A. Napolitano, 2014. Using correlation-based feature selection for a diverse collection of bioinformatics datasets. Proceedings of the 2014 IEEE International Conference on Bioinformatics and Bioengineering (BIBE), November 10-12, 2014, IEEE, Boca Raton, Florida, USA., ISBN:978-1-4799-7502-0, pp: 156-162.

Yildirim, P., 2015. Filter based feature selection methods for prediction of risks in hepatitis disease. Intl. J. Mach. Learn. Comput., 5: 258-263.
CrossRef  |  Direct Link  |  

Zhang, W., J. Tang and N. Wang, 2016. Using the machine learning approach to predict patient survival from high-dimensional survival data. Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), December 15-18, 2016, IEEE, Shenzhen, China, ISBN:978-1-5090-1612-9, pp: 1234-1238.