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Research Article | Open Access |

A Machine Learning Approach for Predicting Nicotine Dependence

Author 1: Mohammad Kharabsheh Author 2: Omar Meqdadi Author 3: Mohammad Alabed Author 4: Sreenivas Veeranki Author 5: Ahmad Abbadi Author 6: Sukaina Alzyoud
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 10, No. 3 · Published 2019 · Cited by 8

DOI: 10.14569/IJACSA.2019.0100323

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.

Keywords

How to Cite this Article

Kharabsheh, M., Meqdadi, O., Alabed, M., Veeranki, S., Abbadi, A., & Alzyoud, S. (2019). A Machine Learning Approach for Predicting Nicotine Dependence. International Journal of Advanced Computer Science and Applications, 10(3). https://doi.org/10.14569/IJACSA.2019.0100323

Kharabsheh, Mohammad, et al.. "A Machine Learning Approach for Predicting Nicotine Dependence." International Journal of Advanced Computer Science and Applications, vol. 10, no. 3, 2019, https://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},
  volume    = {10},
  number    = {3},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Mohammad Kharabsheh and Omar Meqdadi and Mohammad Alabed and Sreenivas Veeranki and Ahmad Abbadi and Sukaina Alzyoud},
  doi       = {10.14569/IJACSA.2019.0100323},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100323}
}

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