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Early Diabetes Identification Enhanced With Metaheuristic Wrapper Based Feature Method
Authors
1 Assistant Professor, Department of CSE, Dhanalakshmi Srinivasan Engineering College, Perambalur, Tamilnadu, India. 2345 UG Student, Department of CSE, Dhanalakshmi Srinivasan Engineering College , Perambalur, Tamilnadu, India.
Article Information
- DOI
- No DOI
- Published In
- Volume 3, Issue 2 (2022)
- Published Online
- March-April 2022
- Pages
- 290-293
- Article Type
- Research Article
- Check for updates
- No DOI available for cross-reference
References
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[2] G. Klöppel, M. Löhr, K. Habich, M. Oberholzer, and P. U. Heitz, ‘‘Islet pathology and the pathogenesis of type 1 and type 2 diabetes mellitus
revisited,’’ Pathol. Immunopathology Res., vol. 4, no. 2, pp. 110–125, 1985, doi: 10.1159/000156969.
[3] International Diabetes Federation—Facts & Figures. Accessed: Dec. 24, 2020. [Online]. Available: https://www.idf.org/aboutdiabetes/ whatis-diabetes/facts-figures.html
[4] C. S. Dangare and S. S. Apte, ‘‘A data mining approach for prediction of heart disease using neural networks,’’ ResearchGate, vol. 3, no. 3,
pp. 30–40, 2012.
[5] S. Smiley. (Jan. 12, 2020). Diagnostic for Heart Disease with Machine Learning. Medium. Accessed: Sep. 19, 2020. [Online]. Available:
https:// towardsdatascience.com/diagnostic-for-heart-disease-with-machinelearning-81b064a3c1dd
[6] R. E. Wright, ‘‘Logistic regression,’’ in Reading and Understanding Multivariate Statistics. Washington, DC, US: American Psychological
Association, 1995, pp. 217–244.
[7] An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression: The American Statistician. Accessed: Sep. 6, 2020. [Online].
Available: https://www.tandfonline.com/doi/abs/10.1080/00031305. 1992.10475879
[8] K. M. Ting and Z. Zheng, ‘‘Improving the performance of boosting for naive Bayesian classification,’’ in Proc. Methodol. Knowl. Discovery
Data Mining, Berlin, Germany, 1999, pp. 296–305, doi: 10.1007/3-540-48912- 6_41.
[9] N. V. Vapnik, Statistical Learning Theory. Hoboken, NJ, USA: Wiley, Sep. 1998. Accessed Sep. 6, 2020.
[10] J. R. Quinlan, ‘‘Induction of decision trees,’’ Mach. Learn., vol. 1, no. 1, pp. 81–106, Mar. 1986, doi: 10.1007/BF00116251.
[11] L. Breiman, ‘‘Random forests,’’ Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.
[12] T. Zheng, W. Xie, L. Xu, X. He, Y. Zhang, M. You, G. Yang, and Y. Chen, ‘‘A machine learning-based framework to identify type 2 diabetes
through electronic health records,’’ Int. J. Med. Informat., vol. 97, pp. 120–127, Jan. 2017, doi: 10.1016/j.ijmedinf.2016.09.014.
[13] D. Sisodia and D. S. Sisodia, ‘‘Prediction of diabetes using classification algorithms,’’ Procedia Comput. Sci., vol. 132, pp. 1578–1585, Jan.
2018, doi: 10.1016/j.procs.2018.05.122.
Learning, 2017.
[2] G. Klöppel, M. Löhr, K. Habich, M. Oberholzer, and P. U. Heitz, ‘‘Islet pathology and the pathogenesis of type 1 and type 2 diabetes mellitus
revisited,’’ Pathol. Immunopathology Res., vol. 4, no. 2, pp. 110–125, 1985, doi: 10.1159/000156969.
[3] International Diabetes Federation—Facts & Figures. Accessed: Dec. 24, 2020. [Online]. Available: https://www.idf.org/aboutdiabetes/ whatis-diabetes/facts-figures.html
[4] C. S. Dangare and S. S. Apte, ‘‘A data mining approach for prediction of heart disease using neural networks,’’ ResearchGate, vol. 3, no. 3,
pp. 30–40, 2012.
[5] S. Smiley. (Jan. 12, 2020). Diagnostic for Heart Disease with Machine Learning. Medium. Accessed: Sep. 19, 2020. [Online]. Available:
https:// towardsdatascience.com/diagnostic-for-heart-disease-with-machinelearning-81b064a3c1dd
[6] R. E. Wright, ‘‘Logistic regression,’’ in Reading and Understanding Multivariate Statistics. Washington, DC, US: American Psychological
Association, 1995, pp. 217–244.
[7] An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression: The American Statistician. Accessed: Sep. 6, 2020. [Online].
Available: https://www.tandfonline.com/doi/abs/10.1080/00031305. 1992.10475879
[8] K. M. Ting and Z. Zheng, ‘‘Improving the performance of boosting for naive Bayesian classification,’’ in Proc. Methodol. Knowl. Discovery
Data Mining, Berlin, Germany, 1999, pp. 296–305, doi: 10.1007/3-540-48912- 6_41.
[9] N. V. Vapnik, Statistical Learning Theory. Hoboken, NJ, USA: Wiley, Sep. 1998. Accessed Sep. 6, 2020.
[10] J. R. Quinlan, ‘‘Induction of decision trees,’’ Mach. Learn., vol. 1, no. 1, pp. 81–106, Mar. 1986, doi: 10.1007/BF00116251.
[11] L. Breiman, ‘‘Random forests,’’ Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.
[12] T. Zheng, W. Xie, L. Xu, X. He, Y. Zhang, M. You, G. Yang, and Y. Chen, ‘‘A machine learning-based framework to identify type 2 diabetes
through electronic health records,’’ Int. J. Med. Informat., vol. 97, pp. 120–127, Jan. 2017, doi: 10.1016/j.ijmedinf.2016.09.014.
[13] D. Sisodia and D. S. Sisodia, ‘‘Prediction of diabetes using classification algorithms,’’ Procedia Comput. Sci., vol. 132, pp. 1578–1585, Jan.
2018, doi: 10.1016/j.procs.2018.05.122.
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