Estimation of Medical Expenses Using Machine Learning

Healthcare providers, patients, and policymakers are all faced with a serious dilemma due to the rising prices of healthcare services. A precise forecast of medical costs can help in resource allocation, insurance pricing, and financial planning for patients and healthcare systems. The implementation of machine learning algorithms to forecast medical costs based on specific patient data is thoroughly examined in this research article. The patient demographics included in the dataset for this study include age, gender, BMI, smoking habits, region, and the number of dependents, among other characteristics. These characteristics were gathered from a variety of healthcare insurance claims. Predictive models are constructed using a variety of regression and ensemble machine learning techniques, including Decision Tree, Random Forest, and Linear Regression. To improve model performance, feature engineering and data pretreatment methods are used. The models are assessed for accuracy and generalizability using measures like mean absolute error (MAE), mean squared error (MSE), and R-squared (R2). Age, BMI, and smoking habits are among the things that are known to have a major impact on predicting medical expenses. The study also examines how geographic region affects cost estimation, highlighting regional variances in healthcare prices. In summary, this study shows how machine learning can accurately forecast medical costs based on patient characteristics. The created models provide insightful cost estimation and can be used to enhance budgeting, resource allocation for healthcare, and insurance pricing. This study offers useful applications with important economic and societal ramifications, contributing to the increasing body of research at the confluence of healthcare and machine learning.