Classification of Smoking Cessation Status Using Various Data Mining Methods

Kartelj, Aleksandar. "Classification of Smoking Cessation Status Using Various Data Mining Methods." Mathematica Balkanica New Series 24.3-4 (2010): 199-205. <http://eudml.org/doc/11357>.

@article{Kartelj2010,
abstract = {AMS Subj. Classification: 62P10, 62H30, 68T01This study examines different approaches of binary classification applied to the prob- lem 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.},
author = {Kartelj, Aleksandar},
journal = {Mathematica Balkanica New Series},
keywords = {Data Mining; Classification; Induction Learning; induction learning},
language = {eng},
number = {3-4},
pages = {199-205},
publisher = {Bulgarian Academy of Sciences - National Committee for Mathematics},
title = {Classification of Smoking Cessation Status Using Various Data Mining Methods},
url = {http://eudml.org/doc/11357},
volume = {24},
year = {2010},
}

TY - JOUR
AU - Kartelj, Aleksandar
TI - Classification of Smoking Cessation Status Using Various Data Mining Methods
JO - Mathematica Balkanica New Series
PY - 2010
PB - Bulgarian Academy of Sciences - National Committee for Mathematics
VL - 24
IS - 3-4
SP - 199
EP - 205
AB - AMS Subj. Classification: 62P10, 62H30, 68T01This study examines different approaches of binary classification applied to the prob- lem 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.
LA - eng
KW - Data Mining; Classification; Induction Learning; induction learning
UR - http://eudml.org/doc/11357
ER -