The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach

The financial catastrophe resulting from the out-of-pocket payments necessary to access and use healthcare systems has been widely studied in literature. aim of this work is predict impact a household will face as result long-term care Spain. These predictions were made using machine learning techniques such LASSO (Least Absolute Shrinkage Selection Operator) penalized regression elastic-net, well algorithms like k-nearest neighbors (KNN), MARS (Multivariate Adaptive Regression Splines), random forest, boosted trees SVM (Support Vector Machine). results reveal that all classification methods performed well, with complex models performing better than simpler ones showing no evidence overfitting. Detecting defining profiles individuals families most likely suffer crucial enabling design policies aimed at protecting vulnerable groups.