Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis-Reference-Cited by-同舟云学术

Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis

Published:2024-01-16 Issue: Volume: Page:
ISSN:
Container-title:CIEES 2023
language:
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Author:

Kossakov Marlen¹,Mukasheva Assel²^ORCID,Balbayev Gani³,Seidazimov Syrym¹,Mukammejanova Dinargul⁴,Sydybayeva Madina⁴

Affiliation:

1. Department of Information Technology, Non-Profit JSC “Almaty University of Power Engineering and Telecommunications Named after Gumarbek Daukeyev”, 050013 Almaty, Kazakhstan

2. School of Information Technology and Engineering, Kazakh-British Technical University, 050000 Almaty, Kazakhstan

3. Academy of Logistics and Transport, 050012 Almaty, Kazakhstan

4. Faculty of Computer Technologies and Cyber Security, International University of Information Technology, 050000 Almaty, Kazakhstan

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/60/1/20/pdf

Reference22 articles.

1. Zhou, Z.-H. (2021). Machine Learning, Springer Nature. XIII, 459p.

2. Raschka, S. (2015). Python Machine Learning, Packt Publishing Ltd.

3. Clustering tuberculosis in children using K-Means based on geographic information system;Wardani;AIP Conf. Proc.,2019

4. Momahhed, S.S., Emamgholipour Sefiddashti, S., Minaei, B., and Shahali, Z. (2023). K-means clustering of outpatient prescription claims for health insureds in Iran. BMC Public Health, 23.

5. Tuberculosis elimination in the era of coronavirus disease 2019 (COVID-19): A moving target;Dookie;Clin. Infect. Dis.,2022

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