Machine Learning to predict the incidence of Retinopathy of Prematurity

Aniket Ray, Vikas Kumar, Balaraman Ravindran, Dr. Lingam Gopal, Dr. Aditya Verma

Retinopathy of Prematurity (ROP) is a disorder afflicting prematurely born infants. ROP can be positively diagnosed a few weeks after birth. The goal of this study is to build an automatic tool for prediction of the incidence of ROP from standard clinical factors recorded at birth for premature babies. The data presents various challenges including mixing of categorical and numeric attributes and noisy data. In this article we present an ensemble classifier—hierarchical committee of random trees—that uses risk factors recorded at birth in order to predict the risk of developing ROP. We empirically demonstrate that our classifier outperforms other state of the art classification approaches.

Subjects: 12. Machine Learning and Discovery; 1.5 Diagnosis

Submitted: Feb 13, 2008

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.