A Machine Learning Approach for Predicting Nicotine Dependence
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 3
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.
Authors and Affiliations
Mohammad Kharabsheh, Omar Meqdadi, Sreenivas Veeranki, Ahmad Abbadi, Sukaina Alzyoud
Keywords
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- EP ID EP498456
- DOI 10.14569/IJACSA.2019.0100323
- Views 113
- Downloads 0
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