Feature importance:Opening a soil-transmitted helminth machine learning model via SHAP  

作　　者：Carlos Matias Scavuzzo Juan Manuel Scavuzzo Micaela Natalia Campero Melaku Anegagrie Aranzazu Amor Aramendia Agustín Benito Victoria Periago 

机构地区：[1]Instituto de Altos Estudios Espaciales Mario Gulich,Univesidad Nacional de Cordoba-Comision Nacional de Actividades Espaciales,Argentina [2]Fundacion Mundo Sano,Madrid,Spain [3]National Centre for Tropical Medicine,Institute of Health Carlos III,Madrid,Spain [4]Fundacion Mundo Sano,Buenos Aires,Argentina [5]Consejo Nacional de Investigaciones Científicasy Tecnicas(CONICET),Buenos Aires,Argentina

出　　处：《Infectious Disease Modelling》2022年第1期262-276,共15页传染病建模（英文）

摘　　要：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.

关 键 词：Shap Shapley Machine learning Remote sensing HOOKWORM Ethiopia 

分 类 号：R532[医药卫生—内科学]