Clinical data analysis and forecasting have made substantial contributions to disease
control, prevention and detection. However, such data usually suffer from highly imbalanced …

In many healthcare applications, datasets for classification may be highly imbalanced due to
the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority …

A considerable amount of health record (HR) data has been stored due to recent advances
in the digitalization of medical systems. However, it is not always easy to analyze HR data …

Healthcare data are well-known to be imbalanced in the data distribution of target classes
where the samples of interest are much fewer than the ordinary samples. When it comes to …

Background An imbalanced dataset is defined as a training dataset that has imbalanced
proportions of data in both interesting and uninteresting classes. Often in biomedical …

Identifying rare but significant healthcare events in massive unstructured datasets has
become a common task in healthcare data analytics. However, imbalanced class distribution …

Imbalanced class distribution is a common problem in a number of fields including medical
diagnostics, fraud detection, and others. It causes bias in classification algorithms leading to …

Most classification algorithms assume that classes are in a balanced state. However,
datasets with class imbalances are everywhere. The classes of actual medical datasets are …

Introduction Medical diagnosis is a crucial step for patient treatment. However, diagnosis is
prone to bias due to imbalanced datasets. To overcome the imbalanced dataset problem …

Background Medical and biological data are commonly with small sample size, missing
values, and most importantly, imbalanced class distribution. In this study we propose a …