Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province.

ABSTRACT:

Background

In order to better assist medical professionals, this study aimed to develop and compare the performance of three models-a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model-to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province.

Methodology/principal findings

Schistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for perfo

SUBMITTER: Li G 

PROVIDER: S-EPMC5831639 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature