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@article{172626,
        author = {Syed Akram and S Santhosh kumar},
        title = {Predictive Analytics For Healthcare Cost  Management Using Gradient Boosting  Regressor and Random Forest},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2982-2988},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172626},
        abstract = {Healthcare cost management is a critical challenge faced by the healthcare industry, with increasing demands for effective and efficient resource utilization. Predictive analytics offers a powerful solution by leveraging data to forecast future costs and optimize decision-making processes. This study explores the application of predictive analytics for healthcare cost management using two advanced machine learning algorithms: Gradient Boosting Regressor (GBR) and Random Forest (RF). The research aims to develop predictive models that accurately estimate healthcare costs based on historical data, patient demographics, treatment types, and other relevant factors. The study involves the following key steps: data collection, preprocessing, feature selection, model training, and evaluation. A comprehensive dataset from a large healthcare provider is used to train and test the models. Gradient Boosting Regressor and Random Forest are chosen for their robustness, ability to handle complex datasets, and superior performance in regression tasks. The models are evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) to assess their accuracy and predictive power. Preliminary results indicate that both GBR and RF models perform well in predicting healthcare costs, with Random Forest showing a slight edge in terms of accuracy and generalization. The study also highlights the importance of feature engineering and selection in enhancing model performance.},
        keywords = {Predictive Analytics, Healthcare Cost Management, Gradient Boosting Regressor, Random Forest, Machine Learning.},
        month = {April},
        }