Diabetes, vertebral column pathologies and Parkinson's disease are three common
diseases which have high prevalence and brought great trouble and pain to billions of …

An important problem in medical data analysis is imbalance dataset. This problem is a
cause of diagnostic mistake. The results of diagnostic affect to life of patients. If a doctor fails …

In recent decades, machine learning algorithms have been used in different fields; one of
the most used fields is the health sector. Biomedical data are usually extensive in size, and …

Learning from data sets that contain very few instances of the minority class usually
produces biased classifiers that have a higher predictive accuracy over the majority class …

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 …

The imbalanced dataset often becomes obstacle in supervised learning process. Imbalance
is case in which the example in training data belonging to one class is heavily outnumber …

Imbalanced data refers to a problem in machine learning where there exists unequal
distribution of instances for each classes. Performing a classification task on such data can …

A well balanced dataset is very important for creating a good prediction model. Medical
datasets are often not balanced in their class labels. Most existing classification methods …

Cardiovascular disease is a substantial cause of mortality and morbidity in the world. In
clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining …