Open AccessThe Application of Artificial Neural Networks and Logistic Regression in the Evaluation of Risk for Dry Eye after Vitrectomy
Author(s) -
Wendi Yang,
Li Wu,
ZhongMing Mei,
YongBing Xiang
Publication year - 2020
Publication title -
journal of ophthalmology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.818
H-Index - 40
eISSN - 2090-0058
pISSN - 2090-004X
DOI - 10.1155/2020/1024926
Subject(s) - vitrectomy , logistic regression , medicine , receiver operating characteristic , univariate , artificial neural network , machine learning , artificial intelligence , regression , statistics , surgery , computer science , multivariate statistics , mathematics , visual acuity
Supervised machine-learning (ML) models were employed to predict the occurrence of dry eye disease (DED) after vitrectomy in this study. The clinical data of 217 patients receiving vitrectomy from April 2017 to July 2018 were used as training dataset; the clinical data of 33 patients receiving vitrectomy from August 2018 to September 2018 were collected as validating dataset. The input features for ML training were selected based on the Delphi method and univariate logistic regression (LR). LR and artificial neural network (ANN) models were trained and subsequently used to predict the occurrence of DED in patients who underwent vitrectomy for the first time during the period. The area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate the predictive accuracy of the ML models. The AUCs with use of the LR and ANN models were 0.741 and 0.786, respectively, suggesting satisfactory performance in predicting the occurrence of DED. When the two models were compared in terms of predictive power, the fitting effect of the ANN model was slightly superior to that of the LR model. In conclusion, both LR and ANN models may be used to accurately predict the occurrence of DED after vitrectomy.