Classification of Smoking Cessation Status Using Various Data Mining Methods

Abstract

This study examines different approaches of binary classification applied to the problem of making distinction between former and current smokers. Prediction is based on data collected in national survey performed by the National center for health statistics of America in 2000. The process consists of two essential parts. The first one determines which attributes are relevant to smokers status, by using methods like basic genetic algorithm and different evaluation functions [1]. The second part is a classification itself, performed by using methods like logistic regression, neural networks and others [2]. Solving these types of problems has its real contributions in decision support systems used by some health institutions.