Work

Quantitative Comparison of Machine Learning Clustering Methods for Tuberculosis Data Analysis

Year: 2024

Type: conference-paper

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.

Source: International Conference on Communications, Information, Electronic and Energy Systems (CIEES)

DOI: 10.3390/engproc2024060020

Authors: Marlen Kossakov, Assel Mukasheva, Gani Balbayev, Syrym Seidazimov, Dinargul Mukammejanova, Madina Sydybayeva

Institutions: Almaty University of Power Engineering and Telecommunications, Kazakh-British Technical University, Mukhametzhan Tynyshbayev ALT University, International Information Technologies University

Language: English

Topic: Advanced Clustering Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4390933320

FWCI: 5.191

Cites: 18

Cited by: 34

Related to: 10