Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis
| Autor: | Marlen Kossakov, Assel Mukasheva, Gani Balbayev, Syrym Seidazimov, Dinargul Mukammejanova, Madina Sydybayeva |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Engineering Proceedings, Vol 60, Iss 1, p 20 (2024) |
| Druh dokumentu: | article |
| ISSN: | 20240600 2673-4591 |
| DOI: | 10.3390/engproc2024060020 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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