Integrated a Suitability Model to Locate the Potential Storage of Stormwater in New York City and Sacramento

In this study, an XGBoost model optimized based on the Newton-Raphson algorithm is proposed for the task of kidney stone risk prediction, which improves the parameter updating strategy of the traditional gradient boosting framework by introducing second-order derivative information. In order to verify the effectiveness of the model, the performance differences between decision trees, random forests, standard XGBoost, CatBoost and the optimized model are systematically compared. The experimental results show that the XGBoost model optimized by the Newton-Raphson algorithm reaches 0.875 in both accuracy and recall indexes, which is significantly better than the other compared models, and its balanced assessment indexes both reflect the accurate identification ability of positive samples and verify the reliability of the overall prediction performance. Particularly noteworthy is that although Random Forest and standard XGBoost perform consistently in accuracy and recall, the differences in precision rate and AUC value reveal the essential difference between the two in feature space division and integration strategy: Random Forest reduces the risk of overfitting through feature randomness, while XGBoost relies on the regularization term to control the model complexity. The research results not only confirm the feasibility of the optimization algorithm in improving the performance of medical prediction models, but also provide an intelligent tool with practical application value for early screening and risk assessment of kidney stones in clinical practice with its stable prediction accuracy of 0.875.