Background: Smoking is a major public health concern in Saudi Arabia, with significant gender differences influencing smoking behavior and cessation. Aim: This study aimed to investigate smoking abstinence self-efficacy (ASE), decisional balance (DB), quitting desire, and their predictors among adult male and female smokers in Saudi Arabia. Methods: A cross-sectional study was conducted using a convenience sample of 375 male and 220 female adult smokers recruited via social media. Data were collected through an online survey assessing personal health, smoking behavior, desire to quit, ASE, and DB. Logistic regression was used to identify predictors of earnest quitting desire, high ASE, and negative DB. Results: Males were more likely to smoke for 10 or more years (70.7% vs. 29.1%), maintain regular smoking patterns (86.9% vs. 54.1%), and exhibit high nicotine dependence (29.3% vs. 6.4%) compared to females. A higher proportion of females (76.8%) than males (66.9%) expressed a strong desire to quit. ASE was generally higher in males, with 49.6% showing average levels, while 46.4% of females had low ASE, particularly in social and positive mood contexts. Females displayed a higher prevalence of negative DB (73.6% vs. 58.1%), indicating greater awareness of smoking’s drawbacks. Both genders acknowledged the cons of smoking, though males perceived fewer pros. Conclusions: A complex interplay of factors influences smoking behavior and cessation among adult smokers. Gender differences also play a crucial role in smoking cessation factors among Saudi adults. Tailored cessation strategies addressing self-efficacy and motivation are recommended to enhance quitting success.

Background: Diabetes mellitus is a global health challenge, especially among homeless people. Early prediction of diabetes can reduce treatment costs and improve interventions. This study aimed to identify predictors of diabetes among homeless adults by utilizing artificial intelligence and providing recommendations for diabetes prevention. Methods: A case-control study of 150 homeless adults in Giza, Egypt (99 diabetes cases and 51 controls), analyzed 43 variables collected through interviews and physiological measures, with missing data imputed. Feature selection using recursive feature elimination and univariate and correlation analyses reduced the predictors to 13 variables. The class imbalance was addressed using synthetic minority over-sampling on the training set. Six models and a stacking ensemble with XGBoost as a meta-learner were evaluated using 5-fold cross-validation and performance metrics, including the accuracy, precision, recall, F1-score, and AUC-ROC. Results: The key predictors included BMI, systolic blood pressure, triceps skinfold thickness, waist circumference, lifestyle factors, comorbidities, diastolic blood pressure, age, medication adherence, educational level, marital status, duration of residence, and diabetes knowledge. Individual classifiers achieved a moderate performance (accuracy: 56.7–70.0%, F1-score: 0.686–0.781). The stacking ensemble substantially outperformed individual models, achieving a 95.45% accuracy, a 100% precision, a 93.75% recall, a 0.968 F1-score, and a 0.979 AUC-ROC on the test set. Conclusions: Machine learning models can reliably predict diabetes. The proposed hybrid stacking model outperformed conventional classifiers in terms of the prediction performance, highlighting the benefits of ensemble learning and sophisticated resampling strategies in dealing with imbalanced medical data. It is recommended that healthcare institutions integrate AI-powered diagnostic assistance technology into clinical processes to aid in the early detection and treatment of diabetes.