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PSO + GWO: a hybrid particle swarm optimization and Grey Wolf optimization based Algorithm for fine-tuning hyper-parameters of convolutional neural networks for Cardiovascular Disease Detection

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Abstract

Cardiovascular diseases are one of the most common health problems worldwide. In this study, a hybrid model is proposed to predict cardiovascular diseases using optimization and deep learning methods. Tuning hyper-parameters of deep learning algorithms is important in the learning process. Therefore, numerous studies on hyper-parameter optimization have been proposed in the literature to improve the performance of deep learning algorithms. In this study, particle swarm optimization (PSO), cat swarm optimization (CSO), and the proposed hybrid of PSO and grey wolf optimization (GWO) algorithms are used for the optimization of the hyper-parameters of the 1D-VGG-16 model. In the proposed study, first, the control parameters of the optimization methods used were fine-tuned. Second, the hyper-parameters of the deep learning method were decided with the help of the optimization methods. Among the optimization methods used, the best success for the 1D-VGG-16 model was achieved with the hybrid PSO + GWO optimization algorithm. To further support the proposed study, the hyper-parameter optimization of the 2D-VGG-16 architecture was carried out with the PSO, CSO, and PSO + GWO hybrid optimization algorithms using the MNIST data set.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the [UCI Machine Learning] repository, [https://archive.ics.uci.edu/ml/datasets/heart+disease].

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  1. Dept. of Software Engineering, Bandırma Onyedi Eylul University, 10250, Balıkesir, Turkey

    Serhat KILIÇARSLAN

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KILIÇARSLAN, S. PSO + GWO: a hybrid particle swarm optimization and Grey Wolf optimization based Algorithm for fine-tuning hyper-parameters of convolutional neural networks for Cardiovascular Disease Detection. J Ambient Intell Human Comput 14, 87–97 (2023). https://doi.org/10.1007/s12652-022-04433-4

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