Karatekin, TamerSancak, SelimÇelik, GökhanTopçuo?lu, SevilayKaratekin, GünerKirci, PinarOkatan, Ali2024-09-112024-09-112019978-172812914-3https://doi.org/10.1109/Deep-ML.2019.00020https://hdl.handle.net/11363/85962019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019 -- 26 August 2019 through 28 August 2019 -- Istanbul -- 153122We have investigated the risk factors that lead to severe retinopathy of prematurity using statistical analysis and logistic regression as a form of generalized additive model (GAM) with pairwise interaction terms (GA2M). In this process, we discuss the trade-off between accuracy and interpretability of these machine learning techniques on clinical data. We also confirm the intuition of expert neonatologists on a few risk factors, such as gender, that were previously deemed as clinically not significant in RoP prediction. © 2019 IEEE.eninfo:eu-repo/semantics/closedAccessGA2M; GAM; generalized additive model; interpretability of machine learning in healthcare; logistic regression; neonatology; Retinopathy of Prematurity (RoP)Conference Object616610.1109/Deep-ML.2019.000202-s2.0-85074884539N/A