Improving human brucellosis susceptibility mapping using effective and simultaneously metaheuristic-based feature selection and hyperparameter tuning
Human Brucellosis, a neglected zoonotic disease, affects 1.6 to 2.1 million people globally each year. In Iran, it has become a significant health concern, with an average annual incidence rate of 19.91 cases per 100,000 people. This study aims to create a reliable Human Brucellosis Susceptibility M...
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| Vydané v: | Acta tropica Ročník 267; s. 107657 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Netherlands
Elsevier B.V
01.07.2025
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| ISSN: | 0001-706X, 1873-6254, 1873-6254 |
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| Abstract | Human Brucellosis, a neglected zoonotic disease, affects 1.6 to 2.1 million people globally each year. In Iran, it has become a significant health concern, with an average annual incidence rate of 19.91 cases per 100,000 people. This study aims to create a reliable Human Brucellosis Susceptibility Map (HBSM) for Mazandaran Province using a hybrid machine learning approach that enhances performance through metaheuristic algorithms for feature and hyperparameter optimization. A transformation function is integrated into these algorithms to reduce computational and time complexities while simultaneously executing feature selection and hyperparameter tuning. Additionally, a two-phase mutation operator is employed to improve the performance of feature selection. The results indicate that the hybrid model of Support Vector Regression-Transformation Mutation Grey Wolf Optimizer (SVR-TMGWO) outperformed other models, achieving RMSE=0.7723, MAE=0.614, MdAE=0.473, and R = 0.536. The predicted HBSM for 2018 identified 68 rural districts in Mazandaran Province as being in the High and Very High susceptibility classes. The susceptibility map can help decision-makers more effectively prevent, control, and manage Human Brucellosis in Mazandaran Province. |
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| AbstractList | Human Brucellosis, a neglected zoonotic disease, affects 1.6 to 2.1 million people globally each year. In Iran, it has become a significant health concern, with an average annual incidence rate of 19.91 cases per 100,000 people. This study aims to create a reliable Human Brucellosis Susceptibility Map (HBSM) for Mazandaran Province using a hybrid machine learning approach that enhances performance through metaheuristic algorithms for feature and hyperparameter optimization. A transformation function is integrated into these algorithms to reduce computational and time complexities while simultaneously executing feature selection and hyperparameter tuning. Additionally, a two-phase mutation operator is employed to improve the performance of feature selection. The results indicate that the hybrid model of Support Vector Regression-Transformation Mutation Grey Wolf Optimizer (SVR-TMGWO) outperformed other models, achieving RMSE=0.7723, MAE=0.614, MdAE=0.473, and R = 0.536. The predicted HBSM for 2018 identified 68 rural districts in Mazandaran Province as being in the High and Very High susceptibility classes. The susceptibility map can help decision-makers more effectively prevent, control, and manage Human Brucellosis in Mazandaran Province. Human Brucellosis, a neglected zoonotic disease, affects 1.6 to 2.1 million people globally each year. In Iran, it has become a significant health concern, with an average annual incidence rate of 19.91 cases per 100,000 people. This study aims to create a reliable Human Brucellosis Susceptibility Map (HBSM) for Mazandaran Province using a hybrid machine learning approach that enhances performance through metaheuristic algorithms for feature and hyperparameter optimization. A transformation function is integrated into these algorithms to reduce computational and time complexities while simultaneously executing feature selection and hyperparameter tuning. Additionally, a two-phase mutation operator is employed to improve the performance of feature selection. The results indicate that the hybrid model of Support Vector Regression-Transformation Mutation Grey Wolf Optimizer (SVR-TMGWO) outperformed other models, achieving RMSE=0.7723, MAE=0.614, MdAE=0.473, and R=0.536. The predicted HBSM for 2018 identified 68 rural districts in Mazandaran Province as being in the High and Very High susceptibility classes. The susceptibility map can help decision-makers more effectively prevent, control, and manage Human Brucellosis in Mazandaran Province.Human Brucellosis, a neglected zoonotic disease, affects 1.6 to 2.1 million people globally each year. In Iran, it has become a significant health concern, with an average annual incidence rate of 19.91 cases per 100,000 people. This study aims to create a reliable Human Brucellosis Susceptibility Map (HBSM) for Mazandaran Province using a hybrid machine learning approach that enhances performance through metaheuristic algorithms for feature and hyperparameter optimization. A transformation function is integrated into these algorithms to reduce computational and time complexities while simultaneously executing feature selection and hyperparameter tuning. Additionally, a two-phase mutation operator is employed to improve the performance of feature selection. The results indicate that the hybrid model of Support Vector Regression-Transformation Mutation Grey Wolf Optimizer (SVR-TMGWO) outperformed other models, achieving RMSE=0.7723, MAE=0.614, MdAE=0.473, and R=0.536. The predicted HBSM for 2018 identified 68 rural districts in Mazandaran Province as being in the High and Very High susceptibility classes. The susceptibility map can help decision-makers more effectively prevent, control, and manage Human Brucellosis in Mazandaran Province. |
| ArticleNumber | 107657 |
| Author | Zandi, Iman Jafari, Ali Alesheikh, Ali Asghar |
| Author_xml | – sequence: 1 givenname: Iman surname: Zandi fullname: Zandi, Iman email: imanzandi.dgh@ut.ac.ir organization: Department of GIS, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, , Iran – sequence: 2 givenname: Ali surname: Jafari fullname: Jafari, Ali email: a.jafari2@email.kntu.ac.ir organization: Department of GIS, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran – sequence: 3 givenname: Ali Asghar orcidid: 0000-0001-9537-9401 surname: Alesheikh fullname: Alesheikh, Ali Asghar email: alesheikh@kntu.ac.ir organization: Department of GIS, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran |
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| Keywords | Metaheuristics algorithms Support vector regression Feature selection Human brucellosis susceptibility mapping Hyperparameter tuning |
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| SubjectTerms | Algorithms brucellosis Brucellosis - epidemiology decision making Disease Susceptibility Feature selection Human brucellosis susceptibility mapping Humans Hyperparameter tuning Iran Iran - epidemiology Machine Learning Metaheuristics algorithms mutation people Support vector regression zoonoses |
| Title | Improving human brucellosis susceptibility mapping using effective and simultaneously metaheuristic-based feature selection and hyperparameter tuning |
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