The Effect of Missing Data Mechanisms on Deep Learning in Binary Classification: A Simulation Study

Investigating the effects of missing data and the methods to overcome problems in statistical models caused by missingness is a significant research topic due to the complex nature of the data, which includes missing observations. The different statistical approaches used in the case of the missing data are complete case analysis and missing data imputation. It is necessary to evaluate missing data mechanisms and patterns to handle missing data issues. However, understanding the missing data mechanism is not easy in relatively large data sets. Recently, deep learning algorithms have been widely used for classification, regression, or clustering tasks in large data sets due to computational advances. The objective of this study is to present the effect of missing data mechanisms on the performance of the deep learning algorithm for binary classification problems.