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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Machine learning is crucial for disease diagnosis, particularly for severe respiratory illnesses like COVID-19.
  • Early diagnosis of COVID-19 is vital to mitigate its severe health impacts.
  • Effective machine learning deployment requires robust hyperparameter optimization and feature selection.

Purpose of the Study:

  • To develop an improved machine learning model for predicting respiratory diseases.
  • To incorporate advanced techniques for hyperparameter optimization and feature selection.
  • To enhance prediction efficacy using ensemble methods and explainable AI.

Main Methods:

  • Hyperparameter optimization using a genetic algorithm.
  • Feature selection via binary grey wolf optimization algorithm.
  • Ensemble model development with a stacking classifier.
  • Explainable AI integration using Shapely adaptive explanations (SHAP) values.
  • Experimentation on the Mexico clinical COVID-19 dataset.

Main Results:

  • The proposed model demonstrated superior prediction accuracy compared to existing methods.
  • The adaboost algorithm, among others, showed excellent performance after hyperparameter optimization.
  • SHAP values were utilized to interpret feature importance, enhancing model transparency.

Conclusions:

  • The developed model offers a promising approach for accurate and interpretable respiratory disease prediction.
  • Optimized machine learning models, especially adaboost, can significantly aid in early disease detection.
  • The integration of ensemble methods and explainable AI improves the reliability and clinical utility of diagnostic systems.