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  1. University of Information Technology
  2. Faculty of Computer Science

Comparative Study of Classification Algorithms for Diabetes and Chronic Kidney Disease Diagnosis

http://hdl.handle.net/20.500.12678/0000006182
http://hdl.handle.net/20.500.12678/0000006182
e84dc7ed-d12a-476d-8c16-08b36af422bc
754bbf57-0879-47ba-b29e-2829502f55c1
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COMPARATIVE COMPARATIVE STUDY OF CLASSIFICATION ALGORITHMS FOR DIABETES AND CHRONIC KIDNEY DISEASE DIAGNOSIS.pdf (500 Kb)
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Publication type
Conference paper
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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.
Keywords
data mining, machine learning, classification
Conference papers
UCSER
17 August, 2019
2nd University Conference on Science, Engineering and Research
Technological University (Kyaukse), Myanmar
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