A Novel Method for Predicting Time of Alcohol Use Based on Personality Traits and Demographic Information

References

• I. R. Whitman, V. Agarwal, G. Nah, J. W. Dukes, E. Vittinghoff, T. A. Dewland, and G. M. Marcus, “Alcohol abuse and cardiac disease,” J. Am. Coll. Cardiol., Vol. 69, pp. 13–24, 2017.
   PubMed Web of Science ®Google Scholar
• N. T. Kearns, H. Blumentha, A. A. Contracto, E. R. Aston, and J. Metrik, “Effect of trauma-related stress after alcohol consumption on perceived likelihood of negative consequences and willingness to drive,” Addict. Behav., Vol. 117, pp. 106836, 2021.
   PubMed Web of Science ®Google Scholar
• J. Han, S. Keedy, C. H. Murray, S. Foxley, and H. Wit, “Acute effects of alcohol on resting-state functional connectivity in healthy young men,” Addict. Behav., Vol. 115, pp. 106786, Apr. 2021.
   PubMed Web of Science ®Google Scholar
• M. H. Palm, S. J. Latendresse, T. Chung, A. E. Hipwell, and C. E. Sartor, “Patterns of bi-directional relations across alcohol use, religiosity, and self-control in adolescent girls,” Addict. Behav., Vol. 114, pp. 106739, 2021.
   PubMed Web of Science ®Google Scholar
• J. Fernández-Solà, “Cardiovascular risks and benefits of moderate and heavy alcohol consumption,” Nat. Rev. Cardiol., Vol. 12, pp. 576–587, 2015.
   PubMed Web of Science ®Google Scholar
• S. A. McKee, T. Falba, S. S. O’Malley, J. Sindelar, and P. G. O’Connor, “Smoking status as a clinical indicator for alcohol misuse in US adults,” Arch. Intern. Med., Vol. 167, pp. 716–721, 2007.
   PubMed Web of Science ®Google Scholar
• W. Roberts, K. E. Moore, M. R. Peltier, T. L. Verplaetse, L. Oberleitner, R. Hacker, and S. A. McKee, “Electronic cigarette use and risk of harmful alcohol consumption in the U.S. population,” Alcohol Clin. Exp. Re., Vol. 42, pp. 2385–2393, 2018.
   PubMed Web of Science ®Google Scholar
• S. V. Katikireddi, E. Whitley, J. Lewsey, L. Gray, and A. H. Leyland, “Socioeconomic status as an effect modifier of alcohol consumption and harm: analysis of linked cohort data,” Lancet Public Health, Vol. 2, pp. 267–276, 2017.
   PubMed Web of Science ®Google Scholar
• S. Malnick, and Y. Maor, “The interplay between alcoholic liver disease, obesity, and the metabolic syndrome,” Visc. Med., Vol. 36, pp. 198–205, June 2020.
   PubMed Web of Science ®Google Scholar
• D. Greco, et al., “Alcohol pattern consumption differently affects the efficiency of macrophage reverse cholesterol transport in vivo,” Nutrients, Vol. 10, pp. 1885, 2018.
   Web of Science ®Google Scholar
• A. Glahn, P. J. Proskynitopoulosa, S. Bleicha, and T. Hillemache, “Pharmacotherapeutic management of acute alcohol withdrawal syndrome in critically Ill patients,” Expert Opin. Pharmacother., Vol. 21, pp. 1083–1092, 2020.
   PubMed Web of Science ®Google Scholar
• V. A. Palzes, et al., “Associations between psychiatric disorders and alcohol consumption levels in an adult primary care population,” Alcohol Clin. Exp. Re., Vol. 44, pp. 2536–2544, 2020.
   PubMed Web of Science ®Google Scholar
• D. Thapinta, S. Skulphan, V. Kitsumban, and C. Longchoopol, “Cognitive behavior therapy self-help booklet to decrease depression and alcohol use among people with alcohol dependence in Thailand,” Issues Ment. Health Nurs., Vol. 38, pp. 964–970, 2017.
   PubMed Web of Science ®Google Scholar
• P. Gache, P. Michaud, U. Landry, C. Accietto, S. Arfaoui, O. Wenger, and J. B. Daeppen, “The alcohol use disorders identification test (AUDIT) as a screening tool for excessive drinking in primary care: reliability and validity of a French version,” Alcohol Clin. Exp. Res., Vol. 29, pp. 2001–2007, Nov 2005.
   PubMed Web of Science ®Google Scholar
• J. B. Saunders, G. Aasland, A. Amundsen, and M. Grant, “Alcohol consumption and related problems among primary health care patients: WHO collaborative project on early detection of persons with harmful alcohol consumption,” Addiction, Vol. 88, pp. 349–362, May 1993.
   PubMed Web of Science ®Google Scholar
• D. G. Buchsbaum, R. G. Buchanan, M. A. Welsh, R. M. Centor, and S. H. Schnoll, “Screening for drinking disorders in the elderly using the CAGE questionnaire,” J. Am. Geriatr. Soc., Vol. 40, pp. 662–665, 1992.
   PubMed Web of Science ®Google Scholar
• F. C. Blow, et al., “The michigan alcoholism screening test – geriatric version (MAST-G): a new elderly-specific screening instrument,” Alcohol Clin. Exp. Re., Vol. 16, pp. 372, 1992.
   Google Scholar
• E. Park, et al., “Unobtrusive and continuous monitoring of alcohol-impaired gait using smart shoes,” Methods Inf. Med., Vol. 56, pp. 74–82, 2017.
   PubMed Web of Science ®Google Scholar
• M. Symons, G. F. X. Feeney, M. R. Gallagher, R. M. Young, and J. P. Connor, “Predicting alcohol dependence treatment outcomes: a prospective comparative study of clinical psychologists versus ‘trained’ machine learning models,” Addiction, Vol. 115, pp. 2164–2175, 2020.
   PubMed Web of Science ®Google Scholar
• R. Whelan, et al., “Neuropsychosocial profiles of current and future adolescent alcohol misusers,” Nature, Vol. 512, pp. 185–189, 2014.
   PubMed Web of Science ®Google Scholar
• K. K. Mak, K. Lee, and C. Park, “Applications of machine learning in addiction studies: a systematic review,” Psychiatryres, Vol. 275, pp. 53–60, 2019.
   Google Scholar
• M. H. Afzali, et al., “Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation,” Addiction, Vol. 114, pp. 662–671, Nov 2018.
   PubMed Web of Science ®Google Scholar
• J. Liu, E. R. Weitzman, and R. Chunara, “Assessing behavioral stages from social media data,” in Proceedings of the Conference on Computer-Supported Cooperative Work. Conference on Computer-Supported Cooperative Work, 2017, pp. 1320–1333.
   Google Scholar
• E. Fehrman, V. Egan, and E. M. Mirkes. “UCI machine learning repository,” 2016.
   Google Scholar
• N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” J. Artif. Intell. Res., Vol. 16, pp. 321–357, 2002.
   Web of Science ®Google Scholar
• A. Fernandez, S. Garcıa, F. Herrera, and N. V. Chawla, “SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary,” J Artif. Intell. Res., Vol. 61, pp. 863905, 2018.
   Web of Science ®Google Scholar
• M. Zeng, B. Zou, F. Wei, X. Liu, and L. Wang, “Effective prediction of three common diseases by combining SMOTE with Tomek links technique for imbalanced medical data,” in IEEE International Conference of Online Analysis and Computing Science, 2016, pp. 225–228.
   Google Scholar
• C. Thomas, and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, Vol. 13, pp. 21–27, 1967.
   Web of Science ®Google Scholar
• J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Ann. Statist., Vol. 29, pp. 1189–1232, 2001.
   Web of Science ®Google Scholar
• A. Liaw, and M. Wiener, “Classification and regression by randomForest,” R News, Vol. 2, pp. 18–22, 2002.
   Google Scholar
• S. Adinugroho, Y. A. Sari, and N. Hidayat, “Drug usage duration classification using extreme learning machine based on personality features,” in International Conference on Sustainable Information Engineering and Technology, 2019.
   Google Scholar