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An Efficient Bio-inspired Bees Colony for Breast Cancer Prediction

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Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 520))

Abstract

Breast cancer has increased mortality rate, one out of eight women have these diseases. The breast cancer is viewed as the second most common type of cancer and it is a big threat to women health and survival. Accurate prediction is a challenging problem with a significant experimental interest. One of the popular methods to predict breast cancer is using the bio-inspired computing. Bio-inspired computing approaches are global optimization algorithms motivated by the natural behaviors of swarms such as ants, birds, fishes and bees. Artificial Bee Colony Algorithm (ABC) is a well-known bio-inspired algorithm, which is robust, easy to implement and has few setting parameters. However, one of ABC disadvantages is that of slow convergence due to poor exploration and exploitation processes. In this paper, a hybrid search strategy namely: Global Guided Artificial Bee Colony (GGABC) algorithm proposed for Recurrent Neural Network training. The proposed GGABC employs a new hybrid population based on a metaheuristic approach to circumvent the deficiency of the standard ABC. The GGABC algorithm was applied to predict the patient’s status of breast cancer. The approach was simulated by the foraging behavior of global best and guided honey bees. The simulation comparative analysis suggested that the GGABC algorithm was found to converge to the optimal solution faster than the ABC, Guided ABC and Global ABC with an improved accuracy. The results of this research can provide critical information to health authorities to effectively manage the risk factors of the breast cancer in an early stage.

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Acknowledgements

This work is supported by University of Malaya BKP research grant no BKP11-2013, and Deanship of Scientific Research, King Khalid University, Abha grant no 525/1438.

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Authors and Affiliations

  1. College and Department of Computer Science, King Khalid University, Abha, Saudi Arabia

    Habib Shah & Nasser Tairan

  2. Department of Information Systems, University of Malaya, Lembah Pantai, 50603, Kuala Lumpur, Malaysia

    Haruna Chiroma & Tutut Herawan

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

Authors
  1. Habib Shah
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  2. Haruna Chiroma
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  3. Tutut Herawan
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  4. Rozaida Ghazali
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  5. Nasser Tairan
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Corresponding author

Correspondence to Habib Shah .

Editor information

Editors and Affiliations

  1. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Malaysia

    Mohamed Othman

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  4. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  5. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Hairulnizam Mahdin

  6. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

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Shah, H., Chiroma, H., Herawan, T., Ghazali, R., Tairan, N. (2019). An Efficient Bio-inspired Bees Colony for Breast Cancer Prediction. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_61

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