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Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data

Mustafa Tunay1, Elnaz Pashaei2, Elham Pashaei1

  • 1Department of Computer Engineering, Istanbul Gelisim University, Istanbul, Turkey.

Computational Intelligence and Neuroscience
|April 4, 2022
PubMed
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The novel hypercube optimization search (HOS) algorithm effectively trains multilayer perceptrons (MLP) for medical data classification. This approach enhances diagnostic accuracy and outperforms existing methods in disease prediction.

Area of Science:

  • Medical Informatics
  • Computational Intelligence
  • Machine Learning

Background:

  • Medical data classification is challenging due to data uncertainty.
  • Multilayer perceptrons (MLP) are widely used neural networks (NNs) for classification.
  • Metaheuristic algorithms offer potential for optimizing NN training.

Purpose of the Study:

  • To propose and evaluate the hypercube optimization search (HOS) algorithm for training MLPs.
  • To enhance the efficacy of MLPs as decision support tools in medical data classification.
  • To assess the performance of HOS-MLP against existing classifiers and metaheuristic optimizers.

Main Methods:

  • The hypercube optimization search (HOS) algorithm was developed, simulating dove foraging behavior.
  • HOS was employed to train multilayer perceptrons (MLP) for medical data classification.

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  • The HOS-MLP model was evaluated on orthopedic, diabetes, coronary heart disease, and breast cancer datasets.

    • Main Results:

    • The HOS-MLP model demonstrated superior performance compared to eleven other classifiers.
    • HOS-trained MLP outperformed MLPs trained by eight established metaheuristic algorithms.
    • Significant improvements were observed in mean square error (MSE), classification accuracy, and convergence rate.

    Conclusions:

    • The hypercube optimization search (HOS) algorithm is a robust and efficient metaheuristic for training MLPs.
    • HOS-MLP provides more accurate medical data classification and disease prediction than conventional methods.
    • This approach offers a promising advancement for decision support systems in healthcare.