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Improving human brucellosis susceptibility mapping using effective and simultaneously metaheuristic-based feature

Iman Zandi1, Ali Jafari2, Ali Asghar Alesheikh3

  • 1Department of GIS, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, , Iran.

Acta Tropica
|May 19, 2025
PubMed
Summary

This study developed a Human Brucellosis Susceptibility Map (HBSM) for Iran using advanced machine learning. The map identifies high-risk areas, aiding in the prevention and control of this significant zoonotic disease.

Keywords:
Feature selectionHuman brucellosis susceptibility mappingHyperparameter tuningMetaheuristics algorithmsSupport vector regression

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Area of Science:

  • Epidemiology
  • Machine Learning
  • Public Health

Background:

  • Human Brucellosis is a neglected zoonotic disease affecting millions globally.
  • Iran faces a significant burden, with high incidence rates.
  • Effective disease mapping is crucial for control strategies.

Purpose of the Study:

  • To develop a reliable Human Brucellosis Susceptibility Map (HBSM) for Mazandaran Province, Iran.
  • To enhance machine learning model performance for disease susceptibility prediction.
  • To identify high-risk areas for targeted interventions.

Main Methods:

  • A hybrid machine learning approach combining Support Vector Regression (SVR) with metaheuristic optimization (Grey Wolf Optimizer).
  • Integration of a transformation function for feature and hyperparameter optimization.
  • Employment of a two-phase mutation operator for improved feature selection.

Main Results:

  • The SVR-TMGWO model demonstrated superior performance (RMSE=0.7723, MAE=0.614, R=0.536).
  • The generated HBSM highlighted 68 rural districts in Mazandaran Province within High and Very High susceptibility classes for 2018.
  • The model effectively reduced computational complexity and improved feature selection.

Conclusions:

  • The developed HBSM provides a valuable tool for public health decision-makers in Mazandaran Province.
  • The hybrid machine learning approach offers a robust method for predicting zoonotic disease susceptibility.
  • Targeted prevention and control measures can be implemented more effectively based on the susceptibility map.