Artificial Neural Network training using metaheuristics for medical data classification: An experimental study

The Artificial Neural Network (ANN) is an important machine learning tool used in medical data classification for disease diagnosis. The learning algorithm in ANN training plays a crucial role in classification performance. Various approaches have been successfully applied as a learning algorithm fo...

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Bibliographic Details
Published in:Expert systems with applications 2022-05, Vol.193, p.116423, Article 116423
Main Authors: Si, Tapas, Bagchi, Jayri, Miranda, Péricles B.C.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Neural Network
Artificial neural networks
Classification
Comparative studies
Decision analysis
Decision making
Geometric accuracy
Heuristic methods
Learning theory
Machine learning
Medical data classification
Metaheuristics
Multi-criteria decision making
Multiple criterion
Neural networks
Training
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Summary:The Artificial Neural Network (ANN) is an important machine learning tool used in medical data classification for disease diagnosis. The learning algorithm in ANN training plays a crucial role in classification performance. Various approaches have been successfully applied as a learning algorithm for ANN training. This paper performs an experimental study that investigates the performance of different metaheuristics as learning algorithms to train the ANN for medical data classification tasks. The experiments are carried out on 15 well-known medical datasets. A comparative study is conducted with the classical Levenberg–Marquardt (LM) and other thirteen recent and relevant metaheuristics. Different evaluation criteria such as accuracy, sensitivity, specificity, precision, Geometric Mean, F-Measure, false-positive rate (FPR) are considered for performance estimation. The classification results are analyzed using Multi-Criteria Decision Making (MCDM) method, and the results with analysis establish that the Equilibrium Optimizer algorithm outperforms all the other algorithms included in the comparative study. •Artificial Neural Network training using metaheuristic algorithms.•An experimental study in medical data classification.•Performance analysis using multi-criteria decision making.•Equilibrium Optimizer shows superior performance over the competitive algorithms.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.116423