Proteomics data are often plagued with missingness issues. These missing values (MVs)
threaten the integrity of subsequent statistical analyses by reduction of statistical power …

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of
significant complications, including cardiovascular disease, kidney failure, diabetic …

Abstract Background and Objective: Although automated Skin Lesion Classification (SLC) is
a crucial integral step in computer-aided diagnosis, it remains challenging due to variability …

Missing values are highly undesirable in real-world datasets. The missing values should be
estimated and treated during the preprocessing stage. With the expansion of nature-inspired …

The COVID-19 pandemic has been spreading worldwide since December 2019, presenting
an urgent threat to global health. Due to the limited understanding of disease progression …

Since the COVID-19 pandemic, several research studies have proposed Deep Learning
(DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when …

Railway accident prediction is of great significance for establishing an early warning
mechanism and preventing the occurrences of accidents. Safety agencies rely on prediction …

Missing values in air quality datasets bring trouble to exploration and decision making about
the environment. Few imputation methods aim at time series air quality data so that they fail …

This work reports the release and the usability of the data handling app, an R application, to
perform an initial evaluation and treatment of the data. This application allows using the data …

In the development of predictive models, the problem of missing data is a critical issue that
traditionally requires a two-step analysis. Data scientists analyze the patterns of missing …