Establishment of a Stacking Machine Learning Model Predicting Cardiac Phenotype in Ectopia Lentis Patients Based on Genotype and Ocular Phenotype.

Abstract

Purpose: To establish a stacking machine learning model for cardiac phenotype prediction in ectopia lentis (EL) patients on the basis of their genotype and ocular phenotype. Methods: We enrolled 151 patients with congenital EL and divided them into three groups according to their echocardiograph (normal group, reflux group, and organic lesion group). All the subjects underwent genetic screening and an up-to-1-year ophthalmic and cardiac follow-up. Patients were randomly divided into training set and validation set in a 3:1 ratio. Six statistically significant parameters based on one-way ANOVA and regression analysis were fed into nine basic algorithms for diagnostic training. Results: Among the three groups, intergroup differences in axial length and central corneal thickness were identified. In genotypes, patients with cysteine-eliminating dominant negative and homozygous deficiency mutations were predisposed to cardiac abnormalities. In addition, the corneal radius of curvature and the mutation domain were also included in the experimental dataset. In the validation set, the diagnostic model achieved a comprehensive accuracy of 75% for predicting cardiac phenotype. Conclusion: We established a reliable machine-learning model which predicts cardiac phenotype using genotype and ocular phenotype in EL patients. This model possibly facilitates effective diagnosis of Marfan syndrome.