Modeling Factors Associated with Smoking Dependence Severity Using Binary Classification-Based Machine Learning: Prediction with Hematological and Demographic Patient Data

Background/Objectives: The Fagerström Test for Nicotine Dependence (FTND) is a subjective, self-report-based instrument used to grade the level of nicotine dependence. Complete blood count (CBC) parameters reflect markers of smoking-related systemic inflammation. In the present study, we aimed to develop a leakage-free and interpretable machine learning approach for classifying FTND level using demographic data, CBC parameters, and duration of smoking exposure.