Expectation of Chronic kidney malady Diagnosis
DOI:
https://doi.org/10.61841/43wvfh80Keywords:
chronic kidney disease, feature subset selection, classification, knowledge discovery, data minig.Abstract
Incessant kidney malady is an all-inclusive normal hindrance whose results can be forestalled or deferred by early identification and fix. Characterization of kidney ailment is fundamental for worldwide improvement and achievement of functional direction. Along these lines, information mining and AI procedures can be utilized to find information and distinguish designs for arrangement. Since there exist includes that cause commotion or are uninformed, the highlight determination issue recognizes a valuable subset of highlights from crude information. The way that dimensionality decrease improves calculation execution, makes quick and minimal effort classifiers, and delivers snappy grouped models makes it well known in information mining and AI methods. In this article, we utilize a lot of channel and wrapper strategies followed by AI methods to characterize ceaseless kidney infection. We show that include choice strategies empower us to perform exact arrangements in the least amount of time utilizing fewer measurements.
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References
1. John, G.H., R. Kohavi, and K. Pfleger. Irrelevant features and the subset selection problem, in Machine Learning: Proceedings of the Eleventh International Conference, 1994.
2. Punch III, W.F., et al., Further Research on Feature Selection and Classification Using Genetic Algorithms, in ICGA, 1993.
3. Akay, M.F., Support vector machines combined with feature selection for breast cancer diagnosis. Expert systems with applications, 2009. 36(2): p. 3240-3247.
4. Xie, J., and C. Wang, Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. ExpertSystems with Applications, 2011. 38(5): p. 5809-5815.
5. Mougiakakou, S.G., et al., Differential diagnosis of CT focal liver lesions using texture features, feature selection, and ensemble-driven classifiers. Artificial Intelligence in Medicine, 2007. 41(1): p. 25-37.
6. Kohavi, R. and G.H. John, Wrappers for feature subset selection. Artificial intelligence, 1997. 97(1): p. 273-324.
7. Jarmulak, J., and S. Craw. Genetic algorithms for feature selection and weighting. in Proceedings of the IJCAI, 1999.
8. Jain, A., and D. Zongker, Feature Selection: Evaluation, Application, and Small Sample Performance. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997. 19(2): p. 153-158.
9. Berk, K.N., Forward and backward stepping in variable selection. Journal of statistical computation and simulation, 1980. 10(3-4): p. 177-185.
10. Siedlecki, W. and J. Sklansky, On automatic feature selection. International Journal of Pattern Recognition and Artificial Intelligence, 1988. 2(02): p. 197-220.
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