Performance Analysis of Health Care Expense Anticipation using Computational Intelligence

The escalating costs of healthcare globally necessitate the development of accurate prediction models to address the financial strain on individuals, families, businesses, and governments. This research employs linear regression models to forecast healthcare expenditures, analyzing the intricate relationship between various factors and costs. A comprehensive literature survey explores topics such as the impact of environmental pollution on healthcare spending, prediction of surgical expenses, budget impact analysis for cancer treatment, and the integration of big data in healthcare infrastructure investments. The methodology involves gathering a diverse dataset encompassing patient demographics, medical history, treatment methods, and associated expenditures. Exploratory data analysis employs Pearson's correlation coefficient to identify trends and correlations between variables. Two linear regression models are proposed and compared, with the second model excluding non-significant variables for enhanced accuracy and interpretability. Results indicate that the second model, with refined feature selection, outperforms the first in terms of mean squared error and R-squared values. A comparative analysis of different studies underscores the diverse findings in healthcare economics, ranging from the impact of CO2 emissions on health to predicting surgery costs and budgetary savings in cancer treatment. The research contributes to the growing body of knowledge in healthcare economics, providing decision-makers with a valuable tool for precise expenditure estimation. The emphasis on linear regression models aids stakeholders in understanding factors influencing healthcare costs, guiding resource allocation, policy creation, and financial planning for more sustainable and effective healthcare systems.