ArticleScientific reports2024
Web application using machine learning to predict cardiovascular disease and hypertension in mine workers.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Perceived Usability as a Factor Associated with Clinical Outcomes in Mobile Health Diabetes Management: A Bayesian Mediation and Equity Analysis.Journal of clinical medicine · 2026Article
- Web application development using multiple regression analysis to predict silicosis risk among stone carvers in Nakhon Ratchasima, Thailand.Journal of occupational medicine and toxicology (London, England) · 2026Article
- Current perspectives and challenges of digital hypertension: artificial intelligence in the management of hypertension.Clinical hypertension · 2026Review
- Web-Based Graphical User Interface Design Integrating MATLAB Server for the Mathematical Model of Human Cardiovascular-Respiratory System.Bioinformatics and biology insights · 2026Article
- A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Multi-omics characterization of RNF157 expression patterns in hepatocellular carcinoma and development of an RNF157-associated prognostic signature.Frontiers in pharmacology · 2025Article
- The action of ginsenoside Rg1 in patients with carotid atherosclerosis: a controlled clinical trial.Frontiers in pharmacology · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines. These high accuracies are crucial for occupational health management, where early detection of health risks can significantly reduce morbidity and mortality among workers exposed to environmental and occupational hazards. The web application provides personalized risk assessments based on key factors, such as age, employment history, family health background, and exposure to environmental risks like dust and noise. By offering actionable insights, the model enables targeted interventions, including workplace modifications and lifestyle recommendations, to mitigate the risk of CVD and HTN. This tool demonstrates the potential of ML to enhance preventive health strategies in high-risk occupational settings.
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Registered trials
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