Web application using machine learning to predict cardiovascular disease and hypertension in mine workers
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, occupat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80919-9 |
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| author | Sohrab Effati Alireza Kamarzardi-Torghabe Fatemeh Azizi-Froutaghe Iman Atighi Somayeh Ghiasi-Hafez |
| author_facet | Sohrab Effati Alireza Kamarzardi-Torghabe Fatemeh Azizi-Froutaghe Iman Atighi Somayeh Ghiasi-Hafez |
| author_sort | Sohrab Effati |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-caa0fd7c68044e99a4de79006624a4c3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-caa0fd7c68044e99a4de79006624a4c32025-01-05T12:26:11ZengNature PortfolioScientific Reports2045-23222024-12-0114111410.1038/s41598-024-80919-9Web application using machine learning to predict cardiovascular disease and hypertension in mine workersSohrab Effati0Alireza Kamarzardi-Torghabe1Fatemeh Azizi-Froutaghe2Iman Atighi3Somayeh Ghiasi-Hafez4Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of MashhadDepartment of Computer Science, Faculty of Mathematical Science, Ferdowsi University of MashhadDepartment of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of MashhadFaculty of Mathematical Science, Islamic Azad UniversityDepartment of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of MashhadAbstract 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.https://doi.org/10.1038/s41598-024-80919-9Care applicationCardiovascular diseaseHypertensionMine WorkersWeb application |
| spellingShingle | Sohrab Effati Alireza Kamarzardi-Torghabe Fatemeh Azizi-Froutaghe Iman Atighi Somayeh Ghiasi-Hafez Web application using machine learning to predict cardiovascular disease and hypertension in mine workers Scientific Reports Care application Cardiovascular disease Hypertension Mine Workers Web application |
| title | Web application using machine learning to predict cardiovascular disease and hypertension in mine workers |
| title_full | Web application using machine learning to predict cardiovascular disease and hypertension in mine workers |
| title_fullStr | Web application using machine learning to predict cardiovascular disease and hypertension in mine workers |
| title_full_unstemmed | Web application using machine learning to predict cardiovascular disease and hypertension in mine workers |
| title_short | Web application using machine learning to predict cardiovascular disease and hypertension in mine workers |
| title_sort | web application using machine learning to predict cardiovascular disease and hypertension in mine workers |
| topic | Care application Cardiovascular disease Hypertension Mine Workers Web application |
| url | https://doi.org/10.1038/s41598-024-80919-9 |
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