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Updated: Jul 21, 2025

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Prediction of Heart Disease Based on Machine Learning Using Jellyfish Optimization Algorithm.

Ahmad Ayid Ahmad1,2, Huseyin Polat1

  • 1Computer Engineering Department, Gazi University, Ankara 06560, Turkey.

Diagnostics (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a highly accurate machine learning model for early heart disease detection. Combining the Jellyfish optimization algorithm with Support Vector Machines (SVM) achieved superior prediction performance.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Data Science

Background:

  • Heart disease remains a leading global cause of mortality, necessitating improved early detection methods.
  • Machine learning (ML) offers a promising avenue for rapid, cost-effective disease diagnosis.
  • Accurate prediction models are crucial for timely intervention and patient outcomes.

Purpose of the Study:

  • To develop a high-performance machine learning model for predicting heart disease using the Cleveland dataset.
  • To optimize feature selection for improved model accuracy and to prevent overfitting.
  • To evaluate the efficacy of the Jellyfish optimization algorithm in conjunction with various ML classifiers.

Main Methods:

  • Feature selection was performed on the Cleveland heart disease dataset using the Jellyfish optimization algorithm to reduce dimensionality.
Keywords:
SVMfeature selectionheart disease diagnosisjellyfish optimizationmachine learning

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  • The dimensionality-reduced dataset was used to train and evaluate multiple machine learning algorithms.
  • Model performance was assessed using key metrics including Sensitivity, Specificity, Accuracy, and Area Under Curve (AUC).

    • Main Results:

    • The Jellyfish optimization algorithm effectively reduced dataset dimensionality, mitigating the curse of dimensionality.
    • The Support Vector Machine (SVM) classifier, trained on the feature-selected dataset, demonstrated the highest performance.
    • The SVM model achieved exceptional results: 98.56% Sensitivity, 98.37% Specificity, 98.47% Accuracy, and 94.48% AUC.

    Conclusions:

    • The synergistic combination of the Jellyfish optimization algorithm and SVM classifier provides a powerful tool for accurate heart disease prediction.
    • This approach offers a robust and efficient method for early detection, potentially saving lives.
    • The study highlights the importance of advanced feature selection techniques in enhancing ML model performance for medical diagnostics.