Assessment of Healthcare Services using Models Based on Support Vector Machines
Anatoli Nachev

Dr. Anatoli Nachev, Business Information Systems, University of Galway, Galway, Ireland.

Manuscript received on 03 November 2022 | Revised Manuscript received on 11 November 2022 | Manuscript Accepted on 15 December 2022 | Manuscript published on 30 December 2022 | PP: 44-49 | Volume-12 Issue-2, December 2022 | Retrieval Number: 100.1/ijeat.B39051212222 | DOI: 10.35940/ijeat.B3905.1212222
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Abstract: This article presents a case study that provides assessment of access to the Irish healthcare system and the services it provides. We explore factors related to unmet heath care needs using recent survey data. Our approach is based on using support vector machines for building predictive models that analyse and measure those factors. The proposed methodology is novel for the domain. Following the behavioural model for access to medical care, we group factors into three categories: predisposing, enabling, and needs, and analyse each group. Experimental results show and measure the primary causes of imbalances and inequalities of treatment in the Irish healthcare system today. 
Keywords: Classification, Healthcare, Machine Learning, Support Vector Machines.
Scope of the Article: Classification