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Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis

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Abstract

In recent decades, artificial neural networks (ANNs) have been extensively applied in different areas such as engineering, medicine, business, education, manufacturing and so on. Nowadays, ANNs are as a hot research in medicine especially in the fields of medical disease diagnosis. To have a high efficiency in ANN, selection of an appropriate architecture and learning algorithm is very important. ANN learning is a complex task and an efficient learning algorithm has a significant role to enhance ANN performance. In this paper, a new meta-heuristic algorithm, centripetal accelerated particle swarm optimization (CAPSO), is applied to evolve the ANN learning and accuracy. The algorithm is based on an improved scheme of particle swarm algorithm and Newton’s laws of motion. The hybrid learning of CAPSO and multi-layer perceptron (MLP) network, CAPSO-MLP, is used to classify the data of nine standard medical datasets of Hepatitis, Heart Disease, Pima Indian Diabetes, Wisconsin Prognostic Breast Cancer, Parkinson’s disease, Echocardiogram, Liver Disorders, Laryngeal 1 and Acute Inflammations. The performance of CAPSO-MLP is compared with those of PSO, gravitational search algorithm and imperialist competitive algorithm on MLP. The efficiency of methods are evaluated based on mean square error, accuracy, sensitivity, specificity, area under the receiver operating characteristics curve and statistical tests of \(t\)-test and Wilcoxon’s signed ranks test. The results indicate that CAPSO-MLP provides more effective performance than the others for medical disease diagnosis especially in term of unseen data (testing data) and datasets with high missing data values.

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Acknowledgments

This research is supported by Research University Grant (03H72), Universiti Teknologi Malaysia. The authors would like to thank Soft Computing Research Group (SCRG), Universiti Teknologi Malaysia (UTM), Johor Bahru Malaysia, for supporting this study. Also, they give kind respect and special thanks to Dr. Zhaleh Beheshti, for proof reading the manuscript and providing valuable comments.

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Authors and Affiliations

  1. Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 , Skudai, Johor, Malaysia

    Zahra Beheshti, Siti Mariyam Hj. Shamsuddin & Siti Sophiayati Yuhaniz

  2. Software Research Group, Nowpardaz Engineering Company, Isfahan, Iran

    Ebrahim Beheshti

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  1. Zahra Beheshti
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  2. Siti Mariyam Hj. Shamsuddin
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  3. Ebrahim Beheshti
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  4. Siti Sophiayati Yuhaniz
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Correspondence to Zahra Beheshti.

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Communicated by D. Liu.

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Beheshti, Z., Shamsuddin, S.M.H., Beheshti, E. et al. Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis. Soft Comput 18, 2253–2270 (2014). https://doi.org/10.1007/s00500-013-1198-0

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