Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

In the realm of medicine, artificial intelligence (AI) has emerged as a transformative force,
harnessing the power to convert raw data into meaningful insights. Rather than supplanting …

Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …

Hepatocellular carcinoma (HCC) is a form of liver cancer that is widespread in Europe,
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …

Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer that necessitates
accurate prediction models for early diagnosis and effective treatment. Machine learning …

Learning from class-imbalanced data continues to be a common and challenging problem in
supervised learning as standard classification algorithms are designed to handle balanced …

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 Machine learning classification algorithms are currently widely used. One of the
main problems faced by classification algorithms is the problem of unbalanced data sets …

Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …

Although cross-validation is a standard procedure for performance evaluation, its joint
application with oversampling remains an open question for researchers farther from the …