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Area of Science:

  • Public Health
  • Infectious Diseases
  • Machine Learning

Background:

  • Sexually transmitted infections (STIs) pose a significant global health and economic challenge, particularly in developing nations.
  • Environmental and social determinants exacerbate the spread of STIs like syphilis.
  • Machine learning (ML) offers potential for improved epidemiological surveillance of infectious diseases.

Purpose of the Study:

  • To evaluate ML models for predicting adverse congenital syphilis outcomes.
  • To optimize healthcare resource allocation and interventions in resource-constrained environments.
  • To enhance epidemiological surveillance for syphilis in Brazil.

Main Methods:

  • Utilized clinical and sociodemographic data from pregnant women in Pernambuco, Brazil's Mãe Coruja Pernambucana Program (PMCP).
  • Implemented rigorous methodology including feature selection, data preprocessing, hyperparameter optimization, and model training/testing.
  • Conducted six experiments with three distinct feature selection techniques.

Main Results:

  • The AdaBoost-BODS-Expert model, incorporating health expert-selected attributes, demonstrated superior performance.
  • This model achieved the best evaluation metrics and gained acceptance from PMCP health experts.
  • Results indicate high reliability for daily classification of congenital syphilis outcomes.

Conclusions:

  • ML, specifically the AdaBoost-BODS-Expert model, provides a reliable tool for congenital syphilis outcome prediction.
  • The model's effectiveness supports its adoption for daily clinical use and epidemiological surveillance.
  • This approach can significantly assist healthcare management in areas with limited resources.