Prediction of postoperative complications of pediatric cataract patients using data mining.

ABSTRACT:

Background

The common treatment for pediatric cataracts is to replace the cloudy lens with an artificial one. However, patients may suffer complications (severe lens proliferation into the visual axis and abnormal high intraocular pressure; SLPVA and AHIP) within 1 year after surgery and factors causing these complications are unknown.

Methods

Apriori algorithm is employed to find association rules related to complications. We use random forest (RF) and Naïve Bayesian (NB) to predict the complications with datasets preprocessed by SMOTE (synthetic minority oversampling technique). Genetic feature selection is exploited to find real features related to complications.

Results

Average classification accuracies in three binary classification problems are over 75%. Second,

SUBMITTER: Zhang K 

PROVIDER: S-EPMC6317183 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature