Purpose This paper reviews the research literature on text mining (TM) with the aim to find
out (1) which cancer domains have been the subject of TM efforts,(2) which knowledge …

Uplift modeling combines machine learning and experimental strategies to estimate the
differential effect of a treatment on individuals' behavior. The paper considers uplift models …

In this study, an artificial neural network (ANN) was developed to determine whether patients
have breast cancer or not. Whether patients have cancer or not and if they have its type can …

During mammogram screening, there is a higher probability that detection of cancers is
missed, and more than 16 percentage of breast cancer is not detected by radiologists. This …

Uplift modeling is a classification method that determines the incremental impact of an action
on a given population. Uplift modeling aims at maximizing the area under the uplift curve …

Several works in the literature use propositional (“black box”) approaches to generate
prediction models. In this work we employ the Inductive Logic Programming technique …

Abstract We introduce Score As You Lift (SAYL), a novel Statistical Relational Learning
(SRL) algorithm, and apply it to an important task in the diagnosis of breast cancer. SAYL …

The main goal of this work is to produce machine learning models that predict the outcome
of a mammography from a reduced set of annotated mammography findings. In the study we …

Overdiagnosis is a phenomenon in which screening identities cancer which may not go on
to cause symptoms or death. Women over 65 who develop breast cancer bear the heaviest …

A typical classification problem involves building a model to correctly segregate instances of
two or more classes. Such a model exhibits differential prediction with respect to given data …