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Analyzing Health Care Dataset using Machine Learning Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 158 - Number 8 |
| Year of Publication: 2017 |
| Authors: B. Tamilvanan, V. Murali Bhaskaran |
10.5120/ijca2017912828
|
B. Tamilvanan, V. Murali Bhaskaran . Analyzing Health Care Dataset using Machine Learning Techniques. International Journal of Computer Applications. 158, 8 ( Jan 2017), 13-15. DOI=10.5120/ijca2017912828
Abstract
This paper mainly deals with different classification algorithms techniques namely Navie Bayes, Sequential Minimal Optimization, Multilayer Perception, and Random Forest. It analyses the breast cancer from UCI machine learning repository. The result of the classification model is precision, recall, F-Measure, time, accuracy. From theses measure, it is observed that naive Bayes algorithms are able to achieve high accuracy and consumed very less time when compare other algorithms.
References
- Arun K. Pujari, Data Mining Techniques, University Press (India) Ltd, 2001.
- Jiawei Han, Micheline Kamber, Data Mining Concepts and Techniques, Elsevier.
- Klosgen W, Zytkow JM, Handbook of Data mining and Knowledge Discovery, Oxford University Press, 2002.
- M.S.Chen, J.hans, P.SYu, Data mining: A overview from a data base perspective, IEEE transaction on Knowledge and data engineering 8(6), pp. 866-883, 1996.
- Quinlan, J. R., C4.5: programs for machine learning. Morgan Kaufmann, Amsterdam, 1993.
- Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q,MotodaH, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou Z-H, Steinbach M, Hand DJ, Steinberg D, Top 10 algorithms in data mining. Knowl Inf Syst 14, pp.1–37, 2008.
- Diana Dumitru, Prediction of recurrent events in breast cancer using the Naive Bayesian classification, Annals of University of Craiova, Math. Comp. Sci. Ser. Volume 36(2), 2009.
- Nurnberger A, Pedrycz W, Kruse R, Neural network approaches. In: Klosgen W, Zytkow JM (eds) Handbook of data mining and knowledge discovery. Oxford University Press, 2002.
- Hammerstrom D, Neural networks at work. IEEE Spectr:pp.26–32 (June), 1993.
- Delen, D., Walker, G., and Kadam, A., Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34, pp.113–127,2005.
- Kaur, H., and Wasan, S. K., Empirical study applications of data mining techniques in healthcare. J. Comput. Sci. 2(2), pp. 194–200, 2006.
- Ubeyli, E. D., Comparison of different classification algorithms in clinical decision making. Expert Syst 24(1), pp. 17–31, 2007.
- Schwarzer, G., Vach, W., and Schumacher, M., On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat. Med. 19, pp. 541–561, 2000.
- http://www.stat.berkeley.edu/~breiman/Random Forests/cc_home.htm#prox Symposium, volume 1, July, 2005.
- Breiman, L., Random Forests, Machine Learning 45(1), 5-32,2001.
- Jehad Ali, Rehanullah Khan, Nasir Ahmad, Imran Maqsood, Random Forests and Decision Trees, International Journal of Computer Science Issues, Vol. 9, Issue 5, No 3, September 2012.
- Sankar K. Pal, Multilayer Perceptron, Fuzzy Sets, and Classification, IEEE transactions on neural networks, vol. 3, no. 5, September 1992.
- https://training.seer.cancer.gov/breast/intro/types.html.
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