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  1. Home
  3. Privacy-preserving Hybrid Ga-lstm Ensemble For Typhoid Detection Using Optimised Clinical Feature Selection.
  1. Home
  3. Privacy-preserving Hybrid Ga-lstm Ensemble For Typhoid Detection Using Optimised Clinical Feature Selection.

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Privacy-Preserving Hybrid GA-LSTM Ensemble for Typhoid Detection Using Optimised Clinical Feature Selection.

Karim Gasmi1, Afrah Alanazi2, Sahar Almenwer1

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Biomedicines
|May 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel framework for diagnosing typhoid fever using genetic algorithms and deep learning, achieving 92% accuracy. The approach ensures patient privacy through federated learning, making it ideal for resource-limited settings.

Keywords:
SDG 3deep learningfeature selectiongenetic algorithmoptimisationtyphoid detection

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Typhoid fever presents a significant public health burden in low-income nations.
  • Accurate diagnosis is challenging due to overlapping symptoms and unreliable conventional methods.
  • Existing diagnostic tools often lack efficiency and patient privacy safeguards.

Purpose of the Study:

  • To develop an automated, reliable, and privacy-preserving diagnostic framework for typhoid fever.
  • To leverage clinical data for enhanced typhoid fever detection.
  • To address limitations in current diagnostic procedures in resource-constrained environments.

Main Methods:

  • A hybrid framework integrating genetic algorithm (GA)-based feature selection and a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) deep learning classifier.
  • Federated learning with the Federated Averaging (FedAvg) algorithm for collaborative model training without raw data sharing.
  • GA was employed to identify the most informative clinical features, reducing redundancy and computational load.

Main Results:

  • The proposed framework achieved 92% accuracy with a strong F1-score and satisfactory sensitivity.
  • The model demonstrated reduced memory requirements and shorter training times compared to using the full feature set.
  • The approach maintained balanced performance even with class imbalance in the data.

Conclusions:

  • The integration of evolutionary feature selection, deep sequential learning, and federated training offers an effective, privacy-aware solution for typhoid fever diagnosis.
  • This framework is well-suited for clinical settings with limited data access and computational resources.
  • The study highlights the potential of AI-driven solutions in improving infectious disease diagnostics in underserved regions.