Naive Bayes Algorithm Using Selection of Correlation Based Featured Selections Features for Chronic Diagnosis Disease

Irfan Santiko, Ikhsan Honggo


Chronic kidney disease is a disease that can cause death, because the pathophysiological etiology resulting in a progressive decline in renal function, and ends in kidney failure. Chronic Kidney Disease (CKD) has now become a serious problem in the world. Kidney and urinary tract diseases have caused the death of 850,000 people each year. This suggests that the disease was ranked the 12th highest mortality rate. Some studies in the field of health including one with chronic kidney disease have been carried out to detect the disease early, In this study, testing the Naive Bayes algorithm to detect the disease on patients who tested positive for negative CKD and CKD. From the results of the test algorithm accuracy value will be compared against the results of the algorithm accuracy before use and after feature selection using feature selection Featured Correlation Based Selection (CFS), it is known that Naive Bayes algorithm after feature selection that is 93.58%, while the naive Bayes without feature selection the result is 93.54% accuracy. Seeing the value of a second accuracy testing Naive Bayes algorithm without using the feature selection and feature selection, testing both these algorithms including the classification is very good, because the accuracy value above 0.90 to 1.00. Included in the excellent classification. higher accuracy results.

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Chronic kidney disease; Naive bayes; CFS;

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Dewi, Sarini Vita. 2014. Analysis of Performance Classification For the diagnosis of Parkinson's Disease. Yogyakarta. Gadjah Mada University.

Gorunescu, Florin. 2011. Data Mining Concepts, Models and Techniques. Romania.

Bradley, P., Andrew, P., 1997. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognit. 30 (7), 11451159.

Breiman, L., 2001. Random forests. Mach. Learn. 45 (1), 532.

Dash, D., Cooper, G.F., 2002. Exact model averaging with naive Bayesian classifiers. In: Proceedings of the 19th European Conference on Machine Learning, Morgan Kaufmann, Sydney, Australia, pp. 9198.

Dash, D., Cooper, G.F., 2004. Model averaging for prediction with discrete Bayesian networks. J. Mach. Learn. Res. 5, 11771203.

Feng, G., Guo, J., Jing, B.Y., Sun, T., 2015. Feature subset selection using naive Bayes for text classification. Pattern Recognit. Lett. 65, 109115.

Forman, G., 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 12891305.

Frank, A., Asuncion, A., 2010. UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, CA,?

Hall, M., 2000. Correlation-based feature selection for discrete and numeric classmachine learning. In: Proceedings of the 17th International Conference on Machine Learning, pp. 359366.

Hall, M., 2007. A decision tree-based attribute weightingfilter for naive Bayes. Knowl.-Based Syst. 20 (2), 120126.

Hall, M., Holmes, G., 2003. Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 15 (6), 14371447.

Javed, K., Maruf, S., Babri, A., Haroon, 2015. A two-stage Markov blanket based feature selection algorithm for text classification. Neurocomputing 157, 91104.

Jiang, L., Cai, Z., Wang, D., 2010. Improving naive Bayes for classification. Int. J. Comput. Appl. 32 (3), 328332.

Khalil, E.H., 2014. A noise tolerantfine tuning algorithm for the naive Bayesian learning algorithm. J. King Saud Univ. Comput. Inf. Sci. 26 (2), 237246.


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International Journal of Informatics and Information Systems (IJIIS)

2579-7069 (Online)
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