Logistic regression in feature selection in data mining

Predictive data mining in clinical medicine deals with learning models to predict patients' health. The models can be devoted to support clinicians in diagnostic, therapeutic, or monitoring tasks. Data mining methods are usually applied in clinical contexts to analyze retrospective data, thus giving healthcare professionals the opportunity to exploit large amounts of data routinely collected during their day-by-day activity. Moreover, clinicians can nowadays take advantage of data mining techniques to deal with the huge amount of research results obtained by molecular medicine, such as genetic or genomic signatures, which may allow transition from population-based to personalized medicine. This paper aims at throwing light on the oldest feature extraction method, namely, the Logistic Regression (LR). LR is useful for situations in which we want to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. LR was used in heart attack classification in coronary Heart Disease (CHD). LR is used in Binary classification.