Overview of artificial neural network models in the biomedical domain.
Overview of artificial neural network models in the biomedical domain.
Biomedical Modeling,V. Renganathan
2019 · DOI: 10.4149/bll_2019_087
Bratislava Medical Journal · 88 Citations
TLDR
The results indicated that the ANN model is more accurate in classifying the dependent variable than the logistic regression model (84.4 % vs 82.9%) and the effect of artificial neural network models in classifies the survival status (event or non-event).
Abstract
AIM
The aim of this paper is to provide an overview of artificial neural network (ANN) in biomedical domain and compare it with the logistic regression model.METHODS
Artificial neural network models and logistic regression models were created and compared using a sample of a modified dataset adapted to the dataset from Framingham Heart Study. R statistical software package is used to create and compare the models.RESULTS
The results indicated that the ANN model is more accurate in classifying the dependent variable than the logistic regression model (84.4 % vs 82.9 %).CONCLUSION
This paper has shown the effect of artificial neural network models in classifying the survival status (event or non-event) (Tab. 2, Fig. 4, Ref. 29).