ArticleInternational journal of environmental research and public health2020
XGBoost-Based Framework for Smoking-Induced Noncommunicable Disease Prediction.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Using elastography-based multilayer perceptron model to evaluate renal fibrosis in chronic kidney disease.Renal failure · 2023Trial
- Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study.European radiology · 2022Trial
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Identifying predictors and assessing causal effect on hypertension risk among adults using Double Machine Learning models: Insights from Bangladesh Demographic and Health Survey.PLoS computational biology · 2025Article
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Prediction of Work-relatedness of Shoulder Musculoskeletal Disorders as by Using Machine Learning.Safety and health at work · 2025Article
- Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.Frontiers in medicine · 2025Article
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study.Renal failure · 2024Article
- Identifying the Drivers Related to Animal Reservoirs, Environment, and Socio-Demography of Human Leptospirosis in Different Community Types of Southern Chile: An Application of Machine Learning Algorithm in One Health Perspective.Pathogens (Basel, Switzerland) · 2024Article
- Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data.Scientific reports · 2024Article
- In Silico Analysis Uncovers FOXA1 as a Potential Biomarker for Predicting Neoadjuvant Chemotherapy Response in Fine-Needle Aspiration Biopsies.Journal of Cancer · 2024Article
- Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role.BMC urology · 2023Article
- Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system.BMC health services research · 2023Article
- Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning.BMC anesthesiology · 2023Article
- Utility of D-dimer in total joint arthroplasty.World journal of orthopedics · 2023Review
- Article
- Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning.Frontiers in cellular and infection microbiology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smoking-induced noncommunicable diseases (SiNCDs) have become a significant threat to public health and cause of death globally. In the last decade, numerous studies have been proposed using artificial intelligence techniques to predict the risk of developing SiNCDs. However, determining the most significant features and developing interpretable models are rather challenging in such systems. In this study, we propose an efficient extreme gradient boosting (XGBoost) based framework incorporated with the hybrid feature selection (HFS) method for SiNCDs prediction among the general population in South Korea and the United States. Initially, HFS is performed in three stages: (I) significant features are selected by t-test and chi-square test; (II) multicollinearity analysis serves to obtain dissimilar features; (III) final selection of best representative features is done based on least absolute shrinkage and selection operator (LASSO). Then, selected features are fed into the XGBoost predictive model. The experimental results show that our proposed model outperforms several existing baseline models. In addition, the proposed model also provides important features in order to enhance the interpretability of the SiNCDs prediction model. Consequently, the XGBoost based framework is expected to contribute for early diagnosis and prevention of the SiNCDs in public health concerns.
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