Diagnosis of Chronic Kidney Disease Using Various Features

Main Article Content

Sena Goral

Abstract

The development of technology provides great convenience in the field of medicine, as in every field. Analyzing the patient's personal information with modern techniques allows specialists to perform faster and more effective treatment. In this study, data with 24 attributes obtained from laboratory results of 400 patients were classified and compared with KNN, decision tree classifier, random forest classifier and adaboost classifier. As a result of classification, Ada Boost Classifier showed the best performance with score value of 98.3%.

Article Details

How to Cite
GORAL, Sena. Diagnosis of Chronic Kidney Disease Using Various Features. Journal of Multidisciplinary Developments, [S.l.], v. 7, n. 1, p. 16-22, mar. 2022. ISSN 2564-6095. Available at: <http://jomude.com/index.php/jomude/article/view/102>. Date accessed: 13 aug. 2022.
Section
Natural Sciences - Regular Research Paper

References

[1] Levey, A. S., & Coresh, J. (2012). Chronic kidney disease. The Lancet, 379(9811), 165-180.

[2] World Kidney Day. (2020). Chronic Kidney Disease. Retrieved from https://www.worldkidneyday.org/facts/chronic-kidneydisease/

[3] National Kidney Foundation. (2020). Global Facts: About Kidney Disease. Retrieved from https://www.kidney.org/kidneydisease/global-facts-aboutkidney-disease#

[4] Al-Hyari, A. Y., Al-Taee, A. M., & Al-Taee, M. A. (2013, December). Clinical decision support system for diagnosis and management of chronic renal failure. In 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (pp. 1-6). IEEE.

[5] Gupta, D., Khare, S., & Aggarwal, A. (2016, April). A method to predict diagnostic codes for chronic diseases using machine learning techniques. In 2016 International Conference on Computing, Communication and Automation (ICCCA) (pp. 281-287). IEEE.

[6] Salekin, A., & Stankovic, J. (2016, October). Detection of chronic kidney disease and selecting important predictive attributes. In 2016 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 262-270). IEEE.

[7] Ogunleye, A., & Wang, Q. G. (2018, June). Enhanced XGBoostbased automatic diagnosis system for chronic kidney disease. In 2018 IEEE 14th International Conference on Control and Automation (ICCA) (pp. 805-810). IEEE.

[8] Chen Z, Zhang X, Zhang Z. Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models. International urology and nephrology 2016; 48.12: 2069-2075.