A Machine Learning Approach for Predicting Nicotine Dependence

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 107
  • Downloads 0

How To Cite

Mohammad Kharabsheh, Omar Meqdadi, Sreenivas Veeranki, Ahmad Abbadi, Sukaina Alzyoud (2019). A Machine Learning Approach for Predicting Nicotine Dependence. International Journal of Advanced Computer Science & Applications, 10(3), 179-184. https://europub.co.uk/articles/-A-498456