This paper investigates the effectiveness of the logistic regression (LR) and random forest
(RF) algorithms for classifying breast cancer using the Breast Cancer Wisconsin Dataset …

Nowadays, with the continuous development of medical equipment, the size and features of
medical database are also increasing rapidly. However, most current cancer detection still …

The abnormality in the cells may lead to cancer. There are around 200 categories of cancer,
wherein after skin cancer, breast cancer is the most common cancer diagnosed in women …

Cancer of the breast is a difficult disease to treat since it weakens the patient's immune
system. Particular interest has lately been shown in the identification of particular immune …

Background: The second leading deadliest disease affecting women worldwide, after lung
cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time …

The principal goal of this study was to explore machine-learning techniques deployed for the
early detection of breast cancer in the United States. Specifically, three algorithms were …

The introduction of algorithms based on machine learning (ML) has revolutionized computer
science by allowing machines to learn without explicit programming. These algorithms …

Nowadays experienced radiologists can perform successful detection of malignant tumors
by examining the histological images or patients' data. However, experts may have different …

Although most cases are identified at a late stage, breast cancer is the most public
malignancy amongst women globally. However, mammography for the analysis of breast …

A number of critical metrics, such as confusion matrix, precision, recall, F1-score, support,
and accuracy, were used to evaluate and rank several machine learning classification …