Interpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurity
2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019, İstanbul, Türkiye, 26 - 28 Ağustos 2019, ss.61-66, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/deep-ml.2019.00020
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.61-66
- Anahtar Kelimeler: GA2M, GAM, generalized additive model, interpretability of machine learning in healthcare, logistic regression, neonatology, Retinopathy of Prematurity (RoP)
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
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.