Vásconez-González, J. et al. Effects of smoking marijuana on the respiratory system: a systematic review. Subst. Abus. 44 (3), 249–260 (2023).
Article
PubMed
Google Scholar
Elisia, I. et al. The effect of smoking on chronic inflammation, immune function and blood cell composition. Sci. Rep. 10 (1), 19480 (2020).
Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Giulietti, F. et al. Pharmacological approach to smoking cessation: an updated review for daily clinical practice. High. Blood Press. Cardiovasc. Prev. 27 (5), 349–362 (2020).
Article
PubMed
PubMed Central
Google Scholar
Jiang, C., Chen, Q. & Xie M. Smoking increases the risk of infectious diseases: A narrative review. Tob. Induc. Dis. 18(July):60. (2020) https://doi.org/10.18332/tid/123845
Kamruzzaman, M., Hossain, A. & Kabir, E. Smoker’s characteristics, general health and their perception of smoking in the social environment: A study of smokers in Rajshahi city, Bangladesh. J. Public. Health 1, 1–2 (2021).
Chang, J. T., Anic, G. M., Rostron, B. L., Tanwar, M. & Chang, C. M. Cigarette smoking reduction and health risks: a systematic review and meta-analysis. Nicotine Tob. Res. 23 (4), 635–642 (2021).
Article
PubMed
Google Scholar
Breslow, L. E. Cigarette smoking and health. Public Health Rep. 95 (5), 451 (1980).
CAS
PubMed
PubMed Central
Google Scholar
Sharma, D., Singh, S., Patil, M. R. & Subhan, A. Medical image processing, disease prediction and report summarization using generative adversarial networks and AIML. In 2024 2nd World Conference on Communication & Computing (WCONF) 2024 Jul 12. 1–5. (IEEE, 2021).
Thakare, V., Batsya, M. T., Jain, M. & Reza, M. Opinion of Health Care Professionals (HCPs) Towards Potential Roles and Impact of AIML (Artificial Intelligence and Machine Learning) in Healthcare: A Questionnaire-Based Survey.
Gerlings, J., Jensen, M. S., Shollo, A. & Explainable, A. I. but explainable to whom? An exploratory case study of xAI in healthcare. In Handbook of Artificial Intelligence in Healthcare. Vol 2. Practicalities and Prospects. 169 – 98. (2022).
Gaber, K. S. & Singla, M. K. Predictive analysis of groundwater resources using random forest regression. J. Artif. Intell. Metaheuristics. 9 (1), 11–19 (2025).
Article
Google Scholar
Elshabrawy, M. A review on waste management techniques for sustainable energy production. Metaheur Optimiz Rev. 3 (2), 47–58 (2025).
Article
Google Scholar
Ammar, M., Javaid, N., Alrajeh, N., Shafiq, M. & Aslam, M. A novel blending approach for smoking status prediction in hidden smokers to reduce cardiovascular disease risk. (IEEE Access., 2024).
Singh, K. N. & Mantri, J. K. A clinical decision support system using rough set theory and machine learning for disease prediction. Intell. Med. 4 (3), 200–208 (2024).
Article
Google Scholar
Singh, K. N. & Mantri, J. K. Clinical decision support system based on RST with machine learning for medical data classification. Multimedia Tools Appl. 83 (13), 39707–39730 (2024).
Article
Google Scholar
Singh, K. N. & Mantri, J. K. An intelligent recommender system using machine learning association rules and rough set for disease prediction from incomplete symptom set. Decis. Analytics J. 11, 100468 (2024).
Article
Google Scholar
McCormick, P. J., Elhadad, N. & Stetson, P. D. Use of semantic features to classify patient smoking status. In AMIA Annual Symposium Proceedings 2008. Vol. 2008. 450. (American Medical Informatics Association, 2008).
Singh, K. N., Mantri, J. K. & Kakulapati, V. Churn prediction of clinical decision support recommender system. In Ambient Intelligence in Health Care: Proceedings of ICAIHC 2022 2022 Nov 23. 371–379. (Springer Nature Singapore, 2022).
Ahmed, I. A., Mohammed, M. A., Hassan, H. M. & Ali, I. A. Relationship between tobacco smoking and hematological indices among Sudanese smokers. J. Health Popul. Nutr. 43 (1), 5 (2024).
Article
PubMed
PubMed Central
Google Scholar
Badicu, G., Zamani Sani, S. H. & Fathirezaie, Z. Predicting tobacco and alcohol consumption based on physical activity level and demographic characteristics in Romanian students. Children 7 (7), 71 (2020).
Article
PubMed
PubMed Central
Google Scholar
Münzel, T. et al. Effects of tobacco cigarettes, e-cigarettes, and waterpipe smoking on endothelial function and clinical outcomes. Eur. Heart J. 41 (41), 4057–4070 (2020).
Article
PubMed
PubMed Central
Google Scholar
Groenhof, T. K. et al. Data mining information from electronic health records produced high yield and accuracy for current smoking status. J. Clin. Epidemiol. 118, 100–106 (2020).
Article
PubMed
Google Scholar
Fan, C. & Gao, F. A new approach for smoking event detection using a variational autoencoder and neural decision forest. IEEE Access. 8, 120835–120849 (2020).
Article
Google Scholar
TonThat, L., Dao, V. T., Tri, H. T. & Le, M. T. A feature subset selection approach for predicting smoking behaviours. In 2023 IEEE Statistical Signal Processing Workshop (SSP) 2023 Jul 2. 145–149. (IEEE, 2023).
De Luna, R. G. et al. SmokeSift: Unraveling smoker and non-smoker individuals through machine learning. In 2024 7th International Conference on Informatics and Computational Sciences (ICICoS) 2024 Jul 17. 84–89. (IEEE, 2024).
Thakur, A., Arunbalaji, C. G., Maddi, A. & Maheswari, B. U. Interpretable predictive modeling for smoking and drinking behavior using SHAP and LIME. In International Conference on Current Trends in Advanced Computing (ICCTAC) 2024 May 8. 1–6. (IEEE, 2024).
Dutta, G. Smoker Status Prediction [Dataset], Kaggle, 2022. https://www.kaggle.com/datasets/gauravduttakiit/smoker-status-prediction.
Lakens, D. Sample size justification. Collabra: Psychol. 8 (1), 33267 (2022).
Article
Google Scholar
Alzakari, S. A., Alhussan, A. A., Qenawy, A. S. & Elshewey, A. M. Early detection of potato disease using an enhanced convolutional neural network-long short-term memory deep learning model. Potato Res. 8, 1–9 (2024).
Khanom, F., Biswas, S., Uddin, M. S. & Mostafiz, R. XEMLPD: an explainable ensemble machine learning approach for Parkinson disease diagnosis with optimized features. Int. J. Speech Technol. 27 (4), 1055–1083 (2024).
Article
Google Scholar
The jamovi project. jamovi (Version 2.6) [Computer software]. https://www.jamovi.orgSCIRP+6jamovi.org+6Bookdown+6 (2025).
Khanom, F., Uddin, M. S. & Mostafiz, R. PD_EBM: an integrated boosting approach based on selective features for unveiling parkinson’s disease diagnosis with global and local explanations. Eng. Rep. 7 (1), e13091 (2025).
Article
Google Scholar
Khanom, F., Mostafiz, R. & Uddin, K. M. Exploring multimodal framework of optimized Feature-Based machine learning to revolutionize the diagnosis of Parkinson’s disease: AI-driven insights. Biomed. Mater. Dev. 24, 1–20 (2025).
Bhat, S. S., Selvam, V. & Ansari, G. A. Predicting life style of early diabetes mellitus using machine learning.
Bhat, S. S., Banu, M. & Ansari, G. A. Predictive analysis for diabetes mellitus prediction using supervised techniques. Int. J. Bioinform. Res. Appl. 20 (1), 78–96 (2024).
Article
Google Scholar
Wani, N. A., Kumar, R. & Bedi, J. Harnessing fusion modeling for enhanced breast cancer classification through interpretable artificial intelligence and in-depth explanations. Eng. Appl. Artif. Intell. 136, 108939 (2024).
Article
Google Scholar
Bhat, S. S. & Ansari, G. A. A domain oriented framework for prediction of diabetes disease and classification of diet using machine learning techniques. In AI and Blockchain in Healthcare 2023 May 1. 203–223. (Springer Nature Singapore, 2023).
Bhat, S. S., Selvam, V., Ansari, G. A., Ansari, M. D. & Rahman, M. H. Prevalence and early prediction of diabetes using machine learning in North kashmir: a case study of district Bandipora. Comput. Intell. Neurosci. 2022 (1), 2789760 (2022).
PubMed
PubMed Central
Google Scholar
El-Kenawy, E. S. et al. Greylag Goose optimization: nature-inspired optimization algorithm. Expert Syst. Appl. 238, 122147 (2024).
Article
Google Scholar
Saha, L., Tripathy, H. K., Gaber, T., El-Gohary, H. & El-kenawy, E. S. Deep churn prediction method for telecommunication industry. Sustainability 15 (5), 4543 (2023).
Article
Google Scholar
Alhussan, A. A. et al. Classification of diabetes using feature selection and hybrid Al-Biruni Earth radius and dipper throated optimization. Diagnostics 13 (12), 2038 (2023).
Article
PubMed
PubMed Central
Google Scholar
Elkenawy, E. S., Alhussan, A. A., Khafaga, D. S., Tarek, Z. & Elshewey, A. M. Greylag Goose optimization and multilayer perceptron for enhancing lung cancer classification. Sci. Rep. 14 (1), 23784 (2024).
Article
CAS
PubMed
PubMed Central
Google Scholar
Alkhammash, E. H. et al. Application of machine learning to predict COVID-19 spread via an optimized BPSO model. Biomimetics 8 (6), 457 (2023).
Article
PubMed
PubMed Central
Google Scholar
Lin, Y. et al. Elucidating tobacco smoke-induced craniofacial deformities: biomarker and MAPK signaling dysregulation unraveled by cross-species multi-omics analysis. Ecotoxicol. Environ. Saf. 288, 117343 (2024).
Article
CAS
PubMed
Google Scholar
Zhang, L. et al. Exposure to smoking and greenspace are associated with allergy medicine use–A study of wastewater in 28 cities of China. Environ. Int. 19, 109291 (2025).
Bilal, A., Shafiq, M., Obidallah, W. J., Alduraywish, Y. A. & Long, H. Quantum computational infusion in extreme learning machines for early multi-cancer detection. J. Big Data. 12 (1), 1–48 (2025).
Article
Google Scholar
Alibrahim, H. & Ludwig, S. A. Hyperparameter optimization: comparing genetic algorithm against grid search and bayesian optimization. In 2021 IEEE Congress on Evolutionary Computation (CEC) 2021 Jun 28. 1551–1559. (IEEE, 2021).
Arnold, C., Biedebach, L., Küpfer, A. & Neunhoeffer, M. The role of hyperparameters in machine learning models and how to tune them. Political Sci. Res. Methods. 12 (4), 841–848 (2024).
Article
Google Scholar
Cisse, A., Evangelopoulos, X., Carruthers, S., Gusev, V. V. & Cooper, A. I. HypBO: Expert-guided chemist-in-the-loop Bayesian search for new materials. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024) (2024).
Rozemberczki, B. et al. The Shapley value in machine learning. In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2022) (De Raedt, L. Ed.) (2022).
Dieber, J. & Kirrane, S. Why model why? Assessing the strengths and limitations of LIME. In Working Paper, Vienna University of Economics and Business (2020).
Bharadi, V. Qlattice environment and Feyn QGraph models—A new perspective toward deep learning. Emerging technologies for healthcare: internet of things and deep learning models. Aug 30, 69–92 (2021).
Google Scholar
Hsu, C. C., Morsalin, S. S., Reyad, M. F. & Shakib, N. Artificial intelligence model interpreting tools: SHAP, LIME, and anchor implementation in CNN model for hand gestures recognition. In International Conference on Technologies and Applications of Artificial Intelligence 2023 Dec 1. 16–29. (Springer Nature Singapore, 2023).
Bilal, A. et al. Quantum chimp-enanced squeezenet for precise diabetic retinopathy classification. Sci. Rep. 15 (1), 12890 (2025).
Article
CAS
PubMed
PubMed Central
Google Scholar
Liu, C. et al. Detection of surface defects in soybean seeds based on improved Yolov9. Sci. Rep. 15 (1), 12631 (2025).
Article
CAS
PubMed
PubMed Central
Google Scholar
Ma, C., Li, Z., Long, H., Bilal, A. & Liu, X. A malware classification method based on directed API call relationships. PloS One. 20 (3), e0299706 (2025).
Article
CAS
PubMed
PubMed Central
Google Scholar
Ahmed, A., Sun, G., Bilal, A., Li, Y. & Ebad, S. A. A hybrid deep learning approach for skin lesion segmentation with dual encoders and Channel-Wise attention. IEEE Access. 13, 42608–42621 (2025).
Article
Google Scholar
Rehman, K. U. et al. A feature fusion attention-based deep learning algorithm for mammographic architectural distortion classification. IEEE J. Biomed. Health Inf. 3. (2025).
Tarek, Z., Alhussan, A. A., Khafaga, D. S., El-Kenawy, E. S. & Elshewey, A. M. A snake optimization algorithm-based feature selection framework for rapid detection of cardiovascular disease in its early stages. Biomed. Signal Process. Control. 102, 107417 (2025).
Article
Google Scholar
Elshewey, A. M., Alhussan, A. A., Khafaga, D. S., Elkenawy, E. S. & Tarek, Z. EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm. Sci. Rep. 14 (1), 24489 (2024).
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhou, J. et al. An integrated CSPPC and BiLSTM framework for malicious URL detection. Sci. Rep. 15 (1), 6659 (2025).
Article
CAS
PubMed
PubMed Central
Google Scholar
Ahmed, A., Sun, G., Bilal, A., Li, Y. & Ebad, S. A. Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model. Sci. Rep. 15 (1), 4815 (2025).
Article
CAS
PubMed
PubMed Central
Google Scholar
Wani, N. A., Kumar, R. & Bedi, J. DeepXplaine: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence. Comput. Methods Programs Biomed. 243, 107879 (2024).
Article
PubMed
Google Scholar
Wani, N. A., Kumar, R., Bedi, J. & Rida, I. Explainable AI-driven IoMT fusion: unravelling techniques, opportunities, and challenges with explainable AI in healthcare. Inform. Fusion. 16, 102472 (2024).
Mohammed, Z. J., Sharba, M. M. & Mohammed, A. A. The Effect of Cigarette Smoking on Haematological Parameters in Healthy College Students in the Capital, Baghdad (European Journal of Molecular & Clinical Medicine, 2022).
Oni, E. T. et al. Non-alcoholic fatty liver disease modifies serum gamma-glutamyl transferase in cigarette smokers. J. Clin. Med. Res. 12 (8), 472 (2020).
Article
CAS
PubMed
PubMed Central
Google Scholar
Pathak, B. G. et al. Tobacco smoking and blood pressure: how are they related among the Indians?–A secondary analysis of National family health survey (NFHS)-4 data. J. Family Med. Prim. Care. 11 (9), 5776–5784 (2022).
Article
PubMed
PubMed Central
Google Scholar
Yang, Y. et al. Interaction between smoking and diabetes in relation to subsequent risk of cardiovascular events. Cardiovasc. Diabetol. 21 (1), 14 (2022).
Article
CAS
PubMed
PubMed Central
Google Scholar
Beklen, A., Sali, N. & Yavuz, M. B. The impact of smoking on periodontal status and dental caries. Tob. Induc. Dis. 20 (2022).
Khoramdad, M. et al. Association between passive smoking and cardiovascular disease: A systematic review and meta‐analysis. IUBMB Life. 72 (4), 677–686 (2020).
Article
CAS
PubMed
Google Scholar
Wani, N. A., Bedi, J., Kumar, R., Khan, M. A. & Rida, I. Synergizing fusion modelling for accurate cardiac prediction through explainable artificial intelligence. IEEE Trans. Consum. Electron. 1. (2024).