Diagnosis of Chronic Kidney Disease Using Various Features

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Published Mar 31, 2022

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Sena Goral
Mehmet Akif Ersoy University, Turkey

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%.

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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: 12 dec. 2024.
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Issue
Vol 7 No 1 (2022): "Creativity": Volume: 7, Issue: 1
Section
Natural Sciences - Regular Research Paper

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References

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