Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis †
Kossakov M. Mukasheva A. Balbayev G. Seidazimov S. Mukammejanova D. Sydybayeva M.
2024Multidisciplinary Digital Publishing Institute (MDPI)
Engineering Proceedings
2024#60Issue 1
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.
clustering , data analysis , machine learning , tuberculosis
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Department of Information Technology, Non-Profit JSC “Almaty University of Power Engineering and Telecommunications Named after Gumarbek Daukeyev”, Almaty, 050013, Kazakhstan
School of Information Technology and Engineering, Kazakh-British Technical University, Almaty, 050000, Kazakhstan
Academy of Logistics and Transport, Almaty, 050012, Kazakhstan
Faculty of Computer Technologies and Cyber Security, International University of Information Technology, Almaty, 050000, Kazakhstan
Department of Information Technology
School of Information Technology and Engineering
Academy of Logistics and Transport
Faculty of Computer Technologies and Cyber Security
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