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
Hlavní autori: Zandi, Iman, Jafari, Ali, Alesheikh, Ali Asghar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands Elsevier B.V 01.07.2025
Predmet:
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
Iran
Metaheuristics algorithms
Support vector regression
Feature selection
Human brucellosis susceptibility mapping
Hyperparameter tuning
ISSN:0001-706X, 1873-6254, 1873-6254
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0001-706X
1873-6254
1873-6254
DOI:10.1016/j.actatropica.2025.107657