Predicting Treatment Costs in Regional Healthcare Systems Using XGBoost: A Case Study

In this study, we use the XGBoost machine learning algorithm to predict treatment costs in regional healthcare systems—focusing on Andhra Pradesh, India. The total amount you may have to pay can a good deal depending on factors such as local regulations, economic conditions and availability of resources. Better prediction of these costs is significant for planning budgets and resource allocation, particularly in areas where spending on healthcare is limited. Machine learning tools such as XGBoost aid healthcare enterprise leaders see more clearly the financial requirements of their services, resulting in smarter interventions. A thorough analysis of how XGBoost model predicts the treatment costs in comparison with regular traditional models, has been conducted for this study. Cost is then determined through an analysis of key factors such as patient demographics, medical history, treatment type and local economic conditions. The results can provide healthcare administrators with some guidance to have an efficient resource planning and contribute in the development of effective interventions on healthcare. The above forms the essence of this work that is illustrative in nature on how machine learning can be better utilized for an evidence-based and more accountable investing solution-oriented healthcare financial management thereby leading to a sustainable or informed decision-making journey.