Evaluation of health care systems using supervised and unsupervised machine learning approaches

Both large-and small-scale health system evaluations are crucial.On a larger scale, it facilitates cross-national comparisons, monitors the advancement of global health objectives, aids in the formulation of public health policy, and ensures efficient use of resources.On a smaller scale, it helps countries figure out how well their health programs are working, see health trends, and find areas that need improvement.A health system is made up of a lot of different people and things, like organizations, institutions, resources, patients, and people who might become patients.All of these people and things work together to give health services to a group of people.This research gathered health indicators from 30 OECD (Organization for Economic Cooperation and Development) nations from 2006 to 2020 and evaluated them through both supervised and unsupervised machine learning methodologies.The findings, demonstrating high accuracy in machine learning methods (exceeding 93.3%), indicate that financial models alone are inadequate for evaluating healthcare systems.The complexity and multidimensional nature of these systems necessitate the inclusion of health indicators in the evaluation process.Even with enough money, people may not get the health outcomes they want if there aren't health policies that are based on the basic ideas of fairness, quality of service delivery, and efficiency that can be put into action.This study employs unsupervised machine learning techniques to analyze health indicators.This helps policymakers, researchers, and other people who are interested in healthcare systems learn more about their strengths and weaknesses.These insights can help policymakers make decisions, make it easier to compare medical systems in different countries, and help healthcare systems get better all the time.