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Performance of small-world feedforward neural networks for the diagnosis of diabetes

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  • Erkaymaz, Okan
  • Ozer, Mahmut
  • Perc, Matjaž

Abstract

We investigate the performance of two different small-world feedforward neural networks for the diagnosis of diabetes. We use the Pima Indians Diabetic Dataset as input. We have previously shown than the Watts–Strogatz small-world feedforward neural network delivers a better classification performance than conventional feedforward neural networks. Here, we compare this performance further with the one delivered by the Newman–Watts small-world feedforward neural network, and we show that the latter is better still. Moreover, we show that Newman–Watts small-world feedforward neural networks yield the highest output correlation as well as the best output error parameters.

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  • Erkaymaz, Okan & Ozer, Mahmut & Perc, Matjaž, 2017. "Performance of small-world feedforward neural networks for the diagnosis of diabetes," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 22-28.
    • Handle: RePEc:eee:apmaco:v:311:y:2017:i:c:p:22-28
    DOI: 10.1016/j.amc.2017.05.010
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    References listed on IDEAS

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    1. M. E. J. Newman & D. J. Watts, 1999. "Scaling and Percolation in the Small-World Network Model," Working Papers 99-05-034, Santa Fe Institute.
    2. Fister, Iztok & Ljubič, Karin & Suganthan, Ponnuthurai Nagaratnam & Perc, Matjaž & Fister, Iztok, 2015. "Computational intelligence in sports: Challenges and opportunities within a new research domain," Applied Mathematics and Computation, Elsevier, vol. 262(C), pages 178-186.
    3. Yilmaz, Ergin & Uzuntarla, Muhammet & Ozer, Mahmut & Perc, Matjaž, 2013. "Stochastic resonance in hybrid scale-free neuronal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5735-5741.
    4. Yilmaz, Ergin & Baysal, Veli & Ozer, Mahmut & Perc, Matjaž, 2016. "Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 538-546.
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    Cited by:

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    2. Shabestari, Payam Sadeghi & Panahi, Shirin & Hatef, Boshra & Jafari, Sajad & Sprott, Julien C., 2018. "A new chaotic model for glucose-insulin regulatory system," Chaos, Solitons & Fractals, Elsevier, vol. 112(C), pages 44-51.
    3. Panahi, Shirin & Aram, Zainab & Jafari, Sajad & Ma, Jun & Sprott, J.C., 2017. "Modeling of epilepsy based on chaotic artificial neural network," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 150-156.
    4. Kaya, Ceren & Erkaymaz, Okan & Ayar, Orhan & Özer, Mahmut, 2018. "Impact of hybrid neural network on the early diagnosis of diabetic retinopathy disease from video-oculography signals," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 164-174.
    5. Sabouri, Amir & Ghasemi, Mahdieh & Mehrabbeik, Mahtab, 2023. "The dynamical analysis of non-uniform neocortical network model in up-down state oscillations," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).

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