Using Conditional Random Forest and Feature Ranking Algorithms to Determine the Relative Importance of the Nicotine Metabolite Ratio (NMR) and Demographic and Behavioral Factors on Nicotine Dependence Severity

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Cigarette smokers have different levels of nicotine dependence, which affect their daily cigarette consumption and ability to quit. Genetic factors play a role, and there is interest in tailored nicotine therapy based on an individual’s rate of nicotine metabolism, which can be measured by the ratio of 3′hydroxycotinine [3HC]-to-cotinine, e.g., the nicotine metabolite ratio [NMR]. However, many other behavioral factors or symptoms are considered important indicators of nicotine dependence. The current study uses machine learning (ML) and traditional statistical methods to rank the importance of NMR and non-NMR factors that contribute to nicotine addiction. Using data from the Pennsylvania Adult Smoking Study (PASS), we found that salivary NMR is a predictor of nicotine dependence in the most commonly used nicotine dependence scales, including the Fagerstrom Test for Nicotine Dependence, Heaviness of Smoking Index, and the Hooked On Nicotine Checklist. However, other predictor variables, such as waking urges, showed higher rankings than NMR, with findings dependent on the modeling approach. These results indicate that other factors besides NMR may be clinically useful for understanding the extent of dependence and approaches to quitting.