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Influence of Missing Values on Artificial Neural Network Performance
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The problem of databases containing missing values is a common one in the medical environment. Researchers must find a way to incorporate the incomplete data into the data set to use those cases in their experiments. Artificial neural networks (ANNs) cannot interpret missing values, and when a database is highly skewed, ANNs have difficulty identjing the factors leading to a rare outcome. This study investigates the impact on ANN performance when predicting neonatal mortality of increasing the number of cases with missing values in the data sets.
Title: Influence of Missing Values on Artificial Neural Network Performance
Description:
The problem of databases containing missing values is a common one in the medical environment.
Researchers must find a way to incorporate the incomplete data into the data set to use those cases in their experiments.
Artificial neural networks (ANNs) cannot interpret missing values, and when a database is highly skewed, ANNs have difficulty identjing the factors leading to a rare outcome.
This study investigates the impact on ANN performance when predicting neonatal mortality of increasing the number of cases with missing values in the data sets.
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