Support Vector Machines: A Literature Review on Their Application in Analyzing Mass Data for Public Health

This study considers the literature on support vector machines (SVMs) in the area of public health data analysis, particularly evaluating their ability to harness big data for disease classification and health predictions. SVMs have been remarkably embraced for two decades in clinical diagnosis, patient management, and prediction of health trends owing to their high precision and robustness. This review suggests the ability of the method to support spatially relevant health system responses through the assessment of SVM advantages and disadvantages in public health and future research agendas, including improving scalability, integrating SVMs with emerging data sources like the Internet of Things (IoT) and genomic data, and enhancing model transparency to support real-world public health decision-making.