Abstract
In the current generation, it has been studied that smoking addiction among the youths is increased exponentially. Since there is a lot of awareness among the people about tobacco use but the youths are exposed to a broader spectrum of the different types of nicotine products like E-cigarettes, or hookah or water pipes, or conventional cigarettes, or dissolvable tobacco, and many more products. Our study aims are to identify some of the demographic factors like age, gender, sex, etc. to predict smoking addiction among the youths of our society. To predict e-cigarette addiction among youths, we considered the wings of Artificial Intelligence (AI) like Machine Learning (ML), and Deep Learning (DL) for better understanding and finding the relationship among the various features. During the data preprocessing we used the elastic net regression technique for feature selection and K-Nearest neighbor for making the accurate predictions. The Hybrid Prediction model was built by the Elastic net regression technique and KNN. It is observed that Elastic net finds a better selection of significant features. The outcome of the suggested pipeline provides high performance on the selection of significant features for the prediction model and provides better accuracy.
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References
Nkiruka, C., Atuegwu, C.O., Laubenbacher, R.C., Perez, M.F., Mortensen, E.M.: Factors associated with e-cigarette use in U.S. young adult never smokers of conventional cigarettes: a machine learning approach. Int. J. Environ. Res. Public Health 17(19), 7271 (2021)
Choi, J., Jung, H.-J., Ferrell, A., Woo, S., Haddad, L.: Machine learning-based nicotine addiction prediction models for youth e-cigarette and waterpipe (Hookah) users. J. Clin. Med. 10(5), 972 (2021)
Pariyadath, V., Stein, E.A., Ross, T.J.: Machine Learning classification of resting state functional connectivity predicts smoking status. Front. Hum. Neurosci. 8, 425 (2014)
https://www.cdc.gov/tobacco/data_statistics/surveys/nyts/data/index.html. Last accessed on 4 June 2021
Ter Braak, C.J.F.: Regression by L1 regularization of smart contrasts and sums (ROSCAS) beats PLS and elastic net in latent variable model. J Chemomet. 23(5)217–228 (2009)
Benowitz, N.L., Burbank, A.D.: Cardiovascular toxicity of nicotine: Implications for electronic cigarette use. Trends Cardiovasc. Med. 26(6), 515–523 (2016)
Wang, D., Connock, M., Barton, P., Fry-Smith, A., Aveyard, P., Moore, D.: ‘Cut down to quit’ with nicotine replacement therapies in smoking cessation: a systematic review of effectiveness and economic analysis. Health Technol. Assess. 12(2), 2008
Kosmider, L., et al.: Carbonyl compounds in electronic cigarette vapors: Effects of nicotine solvent and battery output voltage. Nicotine Tob. Res. 16(10),1319–1326 (2014)
Gentzke, A., et al.: Vital signs: tobacco product use among middle and high school students—United States, 2011–2018. Morb. Mortal. Wkly. Rep. 68(6), 157–164 (2019)
Atuegwu, N.C., Perez, M.F., Oncken, C., Thacker, S., Mead, E.L., Mortensen, E.M.: Association between regular electronic nicotine product use and self-reported periodontal disease status: population assessment of tobacco and health survey. Int. J. Environ. Res. Public Health 16(7), 1263 (2019)
McConnell, R., et al.: Electronic cigarette use and respiratory symptoms in adolescents. Am. J. Respir. Crit. Care. Med. 195(8), 1043–1049 (2017)
Dutra, L.M., Glantz, S.A.: Electronic cigarettes and conventional cigarette use among U.S. adolescents: a cross-sectional study. JAMA Pediatr. 168(7), 610–617 2014
Soneji, S., et al.: Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis. JAMA Pediatr. 171(8), 788–797 (2017)
Shahab, L., Beard, E., Brown, J.: Association of initial e-cigarette and other tobacco product use with subsequent cigarette smoking in adolescents: a cross-sectional, matched control study. Tob. Control 30 (2020)
Mirbolouk, M., et al.: E-cigarette use without a history of combustible cigarette smoking among U.S. adults: behavioral risk factor surveillance system, 2016. Ann. Intern. Med. 170(1), 76–79 (2019)
Cheng, T.: Chemical evaluation of electronic cigarettes. Tob. Control 23, ii11–ii17 (2014)
Sharma, A.: E-cigarettes compromise the gut barrier and trigger inflammation. Iscience 24(2), 102035 (2021)
Wiemken, T.L., Kelley, R.R.: Machine learning in epidemiology and health outcomes research. Annu. Rev. Public Health 41, 21–36 (2020)
Borland, R., Yong, H.H., O’Connor, R.J., Hyland, A., Thompson, M.E.: The reliability and predictive validity of the heaviness of smoking index and its two components: findings from the international tobacco control four country study. Nicotine Tob. Res. 12(Suppl 1), S45–S50 (2010)
Reunanen, J.: Overfitting in making comparisons between variable selection methods. J. Mach. Learn. Res. 3, 137–1382 (2003)
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Pratik, S., Nayak, D.S.K., Prasath, R., Swarnkar, T. (2022). Prediction of Smoking Addiction Among Youths Using Elastic Net and KNN: A Machine Learning Approach. In: Chbeir, R., Manolopoulos, Y., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2021. Lecture Notes in Computer Science(), vol 13119. Springer, Cham. https://doi.org/10.1007/978-3-031-21517-9_20
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