Abstract: An examination of the ability of machine learning methodologies in classifying women Waterpipe (WP) smoker’s level of nicotine dependence is proposed in this work. In this study, we developed a classifier that predicts the level of nicotine dependence for WP tobacco female smokers using a set of novel features relevant to smokers including age, residency, and educational level. The evaluation results show that our approach achieves a recall of 82% when applied on a dataset of female WP smokers in Jordan.

Mohammad Kharabsheh, Omar Meqdadi, Mohammad Alabed, Sreenivas Veeranki, Ahmad Abbadi and Sukaina Alzyoud, “A Machine Learning Approach for Predicting Nicotine Dependence” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100323

@article{Kharabsheh2019,
title = {A Machine Learning Approach for Predicting Nicotine Dependence},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100323},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100323},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Mohammad Kharabsheh and Omar Meqdadi and Mohammad Alabed and Sreenivas Veeranki and Ahmad Abbadi and Sukaina Alzyoud}
}