Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …

Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

Random forest (RF) missing data algorithms are an attractive approach for imputing missing
data. They have the desirable properties of being able to handle mixed types of missing …

Background Nearest neighbor (NN) imputation algorithms are efficient methods to fill in
missing data where each missing value on some records is replaced by a value obtained …

Although, the usefulness of the machine learning (ML) technique in predicting future
outcomes has been established in different domains of applications (eg, heath care), its …

Multiomics data including imaging radiomics and various types of molecular biomarkers
have been increasingly investigated for better diagnosis and therapy in the era of precision …

Missing values are a major problem in medical research. As the complete case analysis
discards useful information, estimation and inference may suffer strongly. Multiple imputation …

In environmental research, missing data are often a challenge for statistical modeling. This
paper addressed some advanced techniques to deal with missing values in a data set …

Background Previous studies suggested that moderate coffee and tea consumption are
associated with lower risk of mortality. However, the association between the combination of …

Multiple imputation (MI) has been widely used for handling missing data in biomedical
research. In the presence of high-dimensional data, regularized regression has been used …