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Engineering Proceedings
Volume 60
Issue 1
10.3390/engproc2024060020
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Proceeding Paper

Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis †

by
Marlen Kossakov
1,
Assel Mukasheva
2,*,
Gani Balbayev
3,
Syrym Seidazimov
1,
Dinargul Mukammejanova
4 and
Madina Sydybayeva
4
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
*
Author to whom correspondence should be addressed.
†
Presented at the 4th International Conference on Communications, Information, Electronic and Energy Systems (CIEES 2023), Plovdiv, Bulgaria, 23–25 November 2023.
Eng. Proc. 2024, 60(1), 20; https://doi.org/10.3390/engproc2024060020
Published: 16 January 2024
(This article belongs to the Proceedings of The 4th International Conference on Communications, Information, Electronic and Energy Systems)
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Abstract

In many fields, data-driven decision making has become essential due to machine learning (ML), which provides insights that improve productivity and quality of life. A basic machine learning approach called clustering helps find comparable data points. Clustering plays a critical role in the identification of patient subgroups and the customisation of treatment in the context of tuberculosis (TB) research. While prior studies have recognized its utility, a comprehensive comparative analysis of multiple clustering methods applied to TB data is lacking. Using TB data, this study thoroughly assesses and contrasts four well-known machine learning clustering algorithms: spectral clustering, DBSCAN, hierarchical clustering, and k-means. To evaluate the quality of a cluster, quantitative measures such as the silhouette score, Davies–Bouldin index, and Calinski–Harabasz index are utilised. The results provide quantitative insights that enhance comprehension of clustering and guide future research.
Keywords: machine learning; clustering; tuberculosis; data analysis machine learning; clustering; tuberculosis; data analysis

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MDPI and ACS Style

Kossakov, M.; Mukasheva, A.; Balbayev, G.; Seidazimov, S.; Mukammejanova, D.; Sydybayeva, M. Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis. Eng. Proc. 2024, 60, 20. https://doi.org/10.3390/engproc2024060020

AMA Style

Kossakov M, Mukasheva A, Balbayev G, Seidazimov S, Mukammejanova D, Sydybayeva M. Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis. Engineering Proceedings. 2024; 60(1):20. https://doi.org/10.3390/engproc2024060020

Chicago/Turabian Style

Kossakov, Marlen, Assel Mukasheva, Gani Balbayev, Syrym Seidazimov, Dinargul Mukammejanova, and Madina Sydybayeva. 2024. "Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis" Engineering Proceedings 60, no. 1: 20. https://doi.org/10.3390/engproc2024060020

APA Style

Kossakov, M., Mukasheva, A., Balbayev, G., Seidazimov, S., Mukammejanova, D., & Sydybayeva, M. (2024). Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis. Engineering Proceedings, 60(1), 20. https://doi.org/10.3390/engproc2024060020

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