Prediction of vestibular schwannoma recurrence using artificial neural network.

ABSTRACT:

Objectives

To compare two statistical models, namely logistic regression and artificial neural network (ANN), in prediction of vestibular schwannoma (VS) recurrence.

Methods

Seven hundred eighty-nine patients with VS diagnosis completed an online survey. Potential predictors for recurrence were derived from univariate analysis by reaching the cut off P value of .05. Those nine potential predictors were years since treatment, surgeon's specialty, resection amount, and having incomplete eye closure, dry eye, double vision, facial pain, seizure, and voice/swallowing problem as a complication following treatment. Multivariate binary logistic regression model was compared with a four-layer 9-5-10-1 feedforward backpropagation ANN for prediction of recurrence.

Results

SUBMITTER: Abouzari M 

PROVIDER: S-EPMC7178452 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature