Machine Learning for Predicting Healthcare Policy Outcomes:Utilizing Machine Learning to Forecast the Outcomes of ProposedHealthcare Policies on Population Health and Economic Indicators

Abstract

This study aims to explore the relationship between healthcare costs, growth rate, and health care in various societies around the world. The paper, therefore, adopts statistical analysis to analyze the variables; Health Expenditure as a percentage of GDP, GDP Growth Rate, Healthcare Access, and GDP per Capita on a cross-sectional sample that encompasses multiple countries and years. The study establishes a direct relationship between health expenditure on the one hand, and the GDP growth rates on the other hand, so it shows that high health expenditure is an engine of better economic development. Further, the assessment brings out the effect of GDP per Capita in determining health care accessibility, thus, pointing toward inequality in the provision of health care according to the income level of the people in the country. Policy implications focus on the need to achieve strategic healthcare financing congruent with the set economic policies to guarantee healthcare service delivery for everyone. The study is useful for users interested in understanding the complex factors that impact the efficiency of healthcare systems and subsequent sustainable socioeconomic advancement in the world.