The Application of Artificial Neural Networks and Logistic Regression in the Evaluation of Risk for Dry Eye after Vitrectomy.
Journal
Journal of ophthalmology
ISSN: 2090-004X
Titre abrégé: J Ophthalmol
Pays: United States
ID NLM: 101524199
Informations de publication
Date de publication:
2020
2020
Historique:
received:
14
10
2019
accepted:
02
04
2020
entrez:
8
5
2020
pubmed:
8
5
2020
medline:
8
5
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32377409
doi: 10.1155/2020/1024926
pmc: PMC7191413
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1024926Informations de copyright
Copyright © 2020 Wan-Ju Yang et al.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
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