Predictive Precision: Unraveling Health Insurance Claim Patterns With Logistic Regression and Decision Trees

Healthcare insurance is a type of insurance policy that covers the risk of medical expenses during any unfortunate crisis. Understanding the pattern in the insurance field is essential for improving policy design, managing risks, and detecting fraud. This study provides a comprehensive comparison of logistic regression with other machine learning models, such as decision tree and support vector machine, to evaluate their effectiveness in predicting health insurance claims. Its performance, evaluated using metrics like precision, recall, and F1 score, shows balanced results, though less robust than the decision tree model. Logistic regression demonstrated balanced performance with an accuracy of 82% and superior interpretability, making it valuable for actionable insights. While the decision tree model achieved the highest accuracy (96%), its complexity, particularly in terms of interpretability and potential overfitting, may limit its real-world applicability. By exploring the trade-offs between accuracy and interpretability, this study provides insurers with practical guidance for optimizing operations, enhancing customer service, and mitigating financial losses.