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Sex-Specific Ensemble Models for Type 2 Diabetes Classification in the Mexican Population.

Miguel M Mendoza-Mendoza1, Samara Acosta-Jiménez1, Carlos E Galván-Tejada1

  • 1Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas, Zacatecas, México.

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|May 13, 2025
PubMed
Summary

This study developed sex-specific models to improve type 2 diabetes (T2D) diagnosis in Mexico. These personalized approaches enhance classification accuracy, aiding early detection and precision medicine for T2D.

Keywords:
machine learningmetamodelpersonalized medicinetype 2 diabetes

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Public Health

Background:

  • Type 2 diabetes (T2D) is a global health concern with rising prevalence, especially in Mexico.
  • Accurate early diagnosis of T2D is challenging, particularly considering biological sex differences.
  • Existing diagnostic methods may not fully account for population-specific and sex-based variations.

Purpose of the Study:

  • To enhance the classification accuracy of T2D in the Mexican population.
  • To develop and apply sex-specific ensemble models for improved T2D diagnosis.
  • To utilize genetic algorithm-based feature selection for identifying key predictive factors.

Main Methods:

  • Analysis of a dataset comprising 1787 Mexican patients, stratified by sex.
  • Application of the GALGO genetic algorithm for sex-specific feature selection.
  • Training and evaluation of various classification models (Random Forest, KNN, SVM, Logistic Regression) and ensemble stacking.

Main Results:

  • The male-specific ensemble model achieved 94% specificity and 96% sensitivity.
  • The female-specific ensemble model demonstrated 96% specificity and 90% sensitivity.
  • Both models exhibited strong overall performance, indicating effectiveness in sex-specific T2D classification.

Conclusions:

  • Sex-specific ensemble models offer a valuable tool for personalized T2D diagnosis in Mexico.
  • Identification of sex-specific predictive features supports precision medicine development.
  • This approach improves diagnostic accuracy and promotes more equitable healthcare.