Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters
Abstract Smoking is a leading cause of various health conditions, including cancer and respiratory diseases. Smokers often face medical restrictions such as limitations in blood and organ donation, reduced effectiveness of medications, and increased surgical complications. These impacts underscore t...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-09409-w |
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| author | S. Aishwarya P. C. Siddalingaswamy Krishnaraj Chadaga |
| author_facet | S. Aishwarya P. C. Siddalingaswamy Krishnaraj Chadaga |
| author_sort | S. Aishwarya |
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| description | Abstract Smoking is a leading cause of various health conditions, including cancer and respiratory diseases. Smokers often face medical restrictions such as limitations in blood and organ donation, reduced effectiveness of medications, and increased surgical complications. These impacts underscore the need for early detection of smoking status to enable timely intervention. This study explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict smoking status based on health parameters, including biosignals and clinical biomarkers. A balanced subset of 2,000 instances was sampled from a publicly available Kaggle dataset comprising clinical and biometric features. Multiple ML models were implemented, including Random Forest Classifier, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors, CatBoost Classifier, and an Artificial Neural Network. The Random Forest Classifier achieved the better performance with an accuracy of 0.80, precision of 0.80, recall of 0.80, and F1-score of 0.79. To enhance model interpretability, four Explainable Artificial Intelligence (XAI) techniques were applied: Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), QLattice, and Anchor. SHAP identified hemoglobin as the most influential predictor, while LIME, QLattice, and Anchor highlighted the role of gamma-glutamyl transferase (t). Interactions between hemoglobin, GTP, and height were associated with more accurate predictions. The integration of ensemble modeling and multiple XAI approaches offers deeper interpretability than prior studies, providing healthcare providers and policymakers with a robust, transparent decision-support tool for targeted intervention strategies. |
| format | Article |
| id | doaj-art-1b4ae6e15c974a3b87e8aa19f7febd58 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1b4ae6e15c974a3b87e8aa19f7febd582025-08-20T04:01:25ZengNature PortfolioScientific Reports2045-23222025-07-0115112310.1038/s41598-025-09409-wExplainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parametersS. Aishwarya0P. C. Siddalingaswamy1Krishnaraj Chadaga2Manipal Institute of Technology, Manipal Academy of Higher EducationManipal Institute of Technology, Manipal Academy of Higher EducationManipal Institute of Technology, Manipal Academy of Higher EducationAbstract Smoking is a leading cause of various health conditions, including cancer and respiratory diseases. Smokers often face medical restrictions such as limitations in blood and organ donation, reduced effectiveness of medications, and increased surgical complications. These impacts underscore the need for early detection of smoking status to enable timely intervention. This study explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict smoking status based on health parameters, including biosignals and clinical biomarkers. A balanced subset of 2,000 instances was sampled from a publicly available Kaggle dataset comprising clinical and biometric features. Multiple ML models were implemented, including Random Forest Classifier, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors, CatBoost Classifier, and an Artificial Neural Network. The Random Forest Classifier achieved the better performance with an accuracy of 0.80, precision of 0.80, recall of 0.80, and F1-score of 0.79. To enhance model interpretability, four Explainable Artificial Intelligence (XAI) techniques were applied: Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), QLattice, and Anchor. SHAP identified hemoglobin as the most influential predictor, while LIME, QLattice, and Anchor highlighted the role of gamma-glutamyl transferase (t). Interactions between hemoglobin, GTP, and height were associated with more accurate predictions. The integration of ensemble modeling and multiple XAI approaches offers deeper interpretability than prior studies, providing healthcare providers and policymakers with a robust, transparent decision-support tool for targeted intervention strategies.https://doi.org/10.1038/s41598-025-09409-wSmokers detectionMachine learningArtificial intelligenceXAIHealth parameters |
| spellingShingle | S. Aishwarya P. C. Siddalingaswamy Krishnaraj Chadaga Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters Scientific Reports Smokers detection Machine learning Artificial intelligence XAI Health parameters |
| title | Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters |
| title_full | Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters |
| title_fullStr | Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters |
| title_full_unstemmed | Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters |
| title_short | Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters |
| title_sort | explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters |
| topic | Smokers detection Machine learning Artificial intelligence XAI Health parameters |
| url | https://doi.org/10.1038/s41598-025-09409-w |
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