Predictive Modeling for Medical Insurance Malpractice Using Random Forest and XGBoost

The study promotes a thorough research effort to create a predictive model specifically for medical malpractice litigation, highlighting the intricate relationships between insurance and medical malpractice claims. The model that has been suggested incorporates sophisticated machine learning methods, particularly the Random Forest and XGBoost algorithms, to improve accuracy and forecasting skills. The study emphasizes the need for a comprehensive strategy that combines state-of-the-art prediction technology with a well-defined theoretical framework. The article provides a comprehensive overview of the topic by carefully examining the underlying factors that give rise to medical malpractice claims, which might range from poor recordkeeping to communication problems. It highlights the financial strain that healthcare providers and patients experience and suggest ways that insurance companies can better control risks, evaluate claims, and encourage preventive treatment. The performance of the suggested Random Forest and XGBoost models is presented in the results and comments, with XGBoost showing better predicting skills. The residual distribution and the accuracy of the models are clearly shown by the visualizations. To sum up, this study lays the groundwork for the development and application of predictive models and provides insurance companies with important information to help them negotiate the intricate terrain of medical malpractice in the healthcare industry.