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 …

The problem of imbalanced data classification often exists in medical diagnosis. Traditional
classification algorithms usually assume that the number of samples in each class is similar …

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 …

Background Over-sampling methods based on Synthetic Minority Over-sampling Technique
(SMOTE) have been proposed for classification problems of imbalanced biomedical data …

The algorithm of C4. 5 decision tree has the advantages of high classification accuracy, fast
calculation speed and comprehensible classification rules, so it is widely used for medical …

With the rapid expansion of data, the problem of data imbalance has become increasingly
prominent in the fields of medical treatment, finance, network, etc. And it is typically solved …

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 …

With the help of machine learning (ML) techniques, the possible errors made by the
pathologists and physicians, such as those caused by inexperience, fatigue, stress and so …

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 …

Abstract The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well
known methods to solve the unequal class distribution problem in imbalanced datasets …