Adopting machine learning to automatically identify candidate patients for corneal refractive surgery.

ABSTRACT: Recently, it has become more important to screen candidates that undergo corneal refractive surgery to prevent complications. Until now, there is still no definitive screening method to confront the possibility of a misdiagnosis. We evaluate the possibilities of machine learning as a clinical decision support to determine the suitability to corneal refractive surgery. A machine learning architecture was built with the aim of identifying candidates combining the large multi-instrument data from patients and clinical decisions of highly experienced experts. Five heterogeneous algorithms were used to predict candidates for surgery. Subsequently, an ensemble classifier was developed to improve the performance. Training (10,561 subjects) and internal validation (2640 subjects) were conducted us

SUBMITTER: Yoo TK 

PROVIDER: S-EPMC6586803 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature