Healthcare expenses have increased significantly worldwide, creating challenges for individuals, insurance providers, and healthcare organizations. Predicting medical costs accurately helps insurance companies determine premiums, enables healthcare providers to optimize resources, and assists patients in financial planning. This research presents a machine learning-based Health Cost Prediction System that estimates an individual's medical expenses based on demographic and health-related factors such as age, gender, Body Mass Index (BMI), smoking habits, number of dependents, and geographic region. Various machine learning algorithms including Linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression are evaluated to determine the most effective model for cost prediction. The proposed system demonstrates how data-driven approaches can improve prediction accuracy and support informed healthcare decision-making.
Machine Learning, Healthcare Analytics, Health Insurance, Cost Prediction, Regression Analysis, Random Forest, Artificial Intelligence.
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