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Comparative Study of Classification Algorithms for Diabetes and Chronic Kidney Disease Diagnosis
http://hdl.handle.net/20.500.12678/0000006182
http://hdl.handle.net/20.500.12678/0000006182e84dc7ed-d12a-476d-8c16-08b36af422bc
754bbf57-0879-47ba-b29e-2829502f55c1
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Publication type | ||||||
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Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Comparative Study of Classification Algorithms for Diabetes and Chronic Kidney Disease Diagnosis | |||||
Language | en | |||||
Publication date | 2019-08-17 | |||||
Authors | ||||||
Aung Nway Oo | ||||||
Khin Thuzar Win | ||||||
Description | ||||||
Now a day, data mining and machine learning methods are used to analyse the medical dataset. These techniques can reduce the number of tests to be taken by a patient, can save cost and can also save time for both, doctors and patients. Classification is a classic data mining technique based on machine learning. There are many classification algorithms that can be used for medical domain. In this paper, Naïve Bayes, Random Forest, KStar and PART classification algorithms are used to classify Diabetes dataset and Chronic Kidney Disease (CKD) dataset. The main objective of this paper is to compare the classification results of each classifier for Diabetes dataset and Chronic Kidney Disease (CKD) dataset. |
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Keywords | ||||||
data mining, machine learning, classification | ||||||
Conference papers | ||||||
UCSER | ||||||
17 August, 2019 | ||||||
2nd University Conference on Science, Engineering and Research | ||||||
Technological University (Kyaukse), Myanmar |