2026-06-14T03:44:47Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/144622025-06-12T14:52:33Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19611

00925njm 22002777a 4500 dc Scavuzzo, Carlos Matias author Scavuzzo, Juan Manuel author Campero, Micaela Natalia author Anegagrie, Melaku author Amor Aramendia, Aranzazu author Benito, Agustin author Periago, Victoria author 2022-02-03 In the field of landscape epidemiology, the contribution of machine learning (ML) to modeling of epidemiological risk scenarios presents itself as a good alternative. This study aims to break with the "black box" paradigm that underlies the application of automatic learning techniques by using SHAP to determine the contribution of each variable in ML models applied to geospatial health, using the prevalence of hookworms, intestinal parasites, in Ethiopia, where they are widely distributed; the country bears the third-highest burden of hookworm in Sub-Saharan Africa. XGBoost software was used, a very popular ML model, to fit and analyze the data. The Python SHAP library was used to understand the importance in the trained model, of the variables for predictions. The description of the contribution of these variables on a particular prediction was obtained, using different types of plot methods. The results show that the ML models are superior to the classical statistical models; not only demonstrating similar results but also explaining, by using the SHAP package, the influence and interactions between the variables in the generated models. This analysis provides information to help understand the epidemiological problem presented and provides a tool for similar studies. Infect Dis Model. 2022 Feb 3;7(1):262-276. 10.1016/j.idm.2022.01.004 2468-0427 Infectious Disease Modelling 35224316 http://hdl.handle.net/20.500.12105/14462 Ethiopia Hookworm Machine learning Remote sensing Shap Shapley Feature importance: Opening a soil-transmitted helminth machine learning model via SHAP