Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is
necessary to distinguish the importance of risk factors of cervical cancer to classify potential …

Abstract The paper presents Imbalance-XGBoost, a Python package that combines the
powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced …

Abstract Knowledge discovery in databases (KDD) is crucial in analyzing data to extract
valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in …

There is often a significant class imbalance in credit scoring datasets, mainly in portfolios of
secured loans such as mortgage loans. A class imbalance occurs when the number of non …

Developments in technology facilitate the use of machine learning methods in medical
fields. In cancer research, the combination of machine learning tools and gene expression …

Purpose Biomedical data frequently contain imbalance characteristics which make
achieving good predictive performance with data-driven machine learning approaches a …

Breast cancer is the most common malignant neoplasm and the leading cause of cancer
mortality among women globally. Current prediction models based on risk factors are …

Accurate diagnosis of breast cancer using automated algorithms continues to be a
challenge in the literature. Although researchers have conducted a great deal of work to …

Breast Cancer is the second most leading cause of death among women. The early
detection of the disease increases the chances of survival of the patient. Therefore, there is …

The Design of effective deep-learning methods for medical image analysis represents a
great challenge given the scarcity of balanced datasets leading to biased results and …