Interpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurity


Karatekin T., Sancak S., Çelik G., Topçuoǧlu S., Karatekin G., Kirci P., ...More

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019, İstanbul, Turkey, 26 - 28 August 2019, pp.61-66 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/deep-ml.2019.00020
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.61-66
  • Keywords: GA2M, GAM, generalized additive model, interpretability of machine learning in healthcare, logistic regression, neonatology, Retinopathy of Prematurity (RoP)
  • Istanbul Gelisim University Affiliated: Yes

Abstract

© 2019 IEEE.We 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.