Reducing Healthcare Maintenance Costs: A Machine Learning Model to Improve Seasonal Vaccine Accessibility and Acceptance Using Vaccination History and Social Determinants of Health

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Purpose: This paper will explain how machine learning models using vaccination history and social determinants of health can predict vaccine acceptancy. With this approach, a health care system can benefit significantly due to enhanced vaccine coverage and avoiding a considerable number of hospitalizations. It especially targets persons with probabilities of vaccine acceptance ranging from 40% to 75%. Methodology: The proposed study will develop predictive ML models using integrated datasets that combine vaccination history with SDOH variables. Multiple ML algorithms will be trained and tested, which will be evaluated on metrics such as accuracy, precision, recall, F1-score, and AUC. A comparative analysis has been performed showing the strengths and weaknesses of each approach. Findings: The article has exemplified that machine learning models are capable of predicating vaccine acceptancy by comparing patterns in historic data and with SDOH factors, hence finding health care-eligible populations. This can be duly utilized by health care systems while seeking to find those populations wherein the probabilities of vaccine acceptances are average, making outreach most effective with optimum efficiency at reduced cost using more effective vaccines. The findings bring front and center the need for ethics practice, robust privacy mechanisms, and cloud-based deployments that can assure scalability and reliability. Unique Contribution to Theory, Policy, and Practice: This current research contributes to the theoretical understanding of vaccine hesitancy through complex modeling interactions that involve vaccination history and SDOH. It could use this to inform health policy on evidence-based strategies for increasing vaccine uptake and efficiently allocating resources. More practically, it lays out the basic technical and ethical structure needed to deploy predictive models in real-world health care settings by incorporating SRE into such initiatives to make them reliable and scalable. It helps bridge the gap through a holistic approach-from data science, to public health policy, to operational implementation.