Ayşe Maraşlı
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ayşe Maraşlı has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Arzu Bulut
Bandirma Onyedi Eylul University
1 shared publication - Büşra Akça
Bandirma Onyedi Eylul University
1 shared publication - Gökhan Aba
Bandirma Onyedi Eylül University, Faculty of Health Science, Health Care Management Dept
1 shared publication - Sinem Kabak
BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ ENSTİTÜSÜ, SAĞLIK YÖNETİMİ (YL) (TEZLİ)
1 shared publication
Latest Publications
- 13 January 2026
- Published by Taylor & Francis in Journal of Ethnicity in Substance Abuse
- p. 1-19
Modeling tobacco dependence using penalized regression and machine learning: The predictive roles of emotions, habits, and intention to quit
This study aimed to predict nicotine dependence levels among university students in Türkiye by modeling the Fagerström Test for Nicotine Dependence (FTND) score as a continuous outcome using penalized regression and machine learning (ML) approaches. A cross-sectional study was conducted with a stratified sample of 1,120 university students, examining 24 psychological, behavioral, motivational, and sociodemographic variables. Six predictive models were compared: linear regression, Least Absolute Shrinkage and Selection Operator (LASSO), Ridge regression, Elastic Net, Support Vector Regression (SVR), and tuned extreme gradient boosting (XGBoost). Penalized regression models and the tuned XGBoost algorithm demonstrated largely comparable predictive performance. XGBoost achieved the highest predictive accuracy (R2 = 0.621; adjusted R2 = 0.575) and the lowest RMSE (1.678). However, the LASSO model yielded nearly identical performance (R2 = 0.620; RMSE = 1.680) while offering greater interpretability through embedded variable selection. Given the negligible difference in predictive accuracy, LASSO was selected as the primary model due to its clinical utility and ability to identify modifiable risk factors. The LASSO model explained approximately 59% of the variance in FTND scores and identified 11 significant predictors, including emotional symptoms (e.g., irritability and depressive mood), habitual smoking, intention to quit, daily cigarette consumption, and family smoking. This study highlights the value of interpretable modeling approaches and underscores that nicotine dependence is shaped by a complex interplay of psychosocial, behavioral, and demographic determinants in addition to physiological factors.
- 13 January 2026
- Published by Taylor & Francis in Journal of Ethnicity in Substance Abuse
- p. 1-19
Modeling tobacco dependence using penalized regression and machine learning: The predictive roles of emotions, habits, and intention to quit
This study aimed to predict nicotine dependence levels among university students in Türkiye by modeling the Fagerström Test for Nicotine Dependence (FTND) score as a continuous outcome using penalized regression and machine learning (ML) approaches. A cross-sectional study was conducted with a stratified sample of 1,120 university students, examining 24 psychological, behavioral, motivational, and sociodemographic variables. Six predictive models were compared: linear regression, Least Absolute Shrinkage and Selection Operator (LASSO), Ridge regression, Elastic Net, Support Vector Regression (SVR), and tuned extreme gradient boosting (XGBoost). Penalized regression models and the tuned XGBoost algorithm demonstrated largely comparable predictive performance. XGBoost achieved the highest predictive accuracy (R2 = 0.621; adjusted R2 = 0.575) and the lowest RMSE (1.678). However, the LASSO model yielded nearly identical performance (R2 = 0.620; RMSE = 1.680) while offering greater interpretability through embedded variable selection. Given the negligible difference in predictive accuracy, LASSO was selected as the primary model due to its clinical utility and ability to identify modifiable risk factors. The LASSO model explained approximately 59% of the variance in FTND scores and identified 11 significant predictors, including emotional symptoms (e.g., irritability and depressive mood), habitual smoking, intention to quit, daily cigarette consumption, and family smoking. This study highlights the value of interpretable modeling approaches and underscores that nicotine dependence is shaped by a complex interplay of psychosocial, behavioral, and demographic determinants in addition to physiological factors.