Bratislavske lekarske listy · 2019
Overview of artificial neural network models in the biomedical domain.
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Renganathan V
Аннотация
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).
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).