Márcia Andréa Costa Gomes, Adalgisa de Sousa Paiva Ferreira, Antonio Augusto Moura da Silva, Eliane Rabelo de Souza
Hepatitis A: seroprevalence and associated factors among schoolchildren of São Luís (MA), Brazil
Revista brasileira de epidemiologia = Brazilian journal of epidemiology
study focused comparing distributed learning models centralized local models assessing efficacy predicting specific delivery patientrelated outcomes obstetrics using realworld data predictions focus key moments obstetric care process including discharge various stages hospitalization analysis using 6 different machine learning methods like decision trees bayesian methods stochastic gradient descent knearest neighbors adaboost multilayer perceptron 19 different variables various distributions types revealed distributed models least equal often superior centralized versions local versions also describe thoroughly preprocessing stage order help others implement method realworld scenarios preprocessing steps included cleaning harmonizing missing values handling missing data encoding categorical variables multisite logic even though type machine learning model distribution outcome variable impact result reached results 66 superior centralized local counterpart 77 better centralized adaboost experiments also shed light preprocessing steps required implement distributed models realworld scenario results advocate distributed learning promising tool applying machine learning clinical settings particularly privacy data security paramount thus offering robust solution privacyconcerned clinical applications