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A fuzzy WASD neuronet with application in breast cancer prediction

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Abstract

Cancer is one of the world’s leading causes of human mortality, and the most prevalent type is breast cancer. However, when diagnosed early, breast cancer may be treated. In this paper, a 5-layer feed-forward neuronet model, trained by a novel fuzzy WASD (weights-and-structure-determination) algorithm, called FUZWASD, is introduced and employed to predict whether the breast cancer is benign or malignant. In general, WASD-trained neuronets are known to overcome the limitations of traditional back-propagation neuronets, including slow training speed and local minimum; however, multi-input WASD-trained neuronets with no dimension explosion weakness are few. In this work, a novel FUZWASD algorithm for training neuronets is modeled by embedding a fuzzy logic controller (FLC) in a WASD algorithm, and a multi-input FUZWASD neuronet (MI-FUZWASDN) model for classification problems with no dimension explosion weakness is proposed. The FUZWASD algorithm uses a FLC to map the input data into a specific interval that enhances the accuracy of the weights-direct-determination (WDD) method. In this way, the FUZWASD algorithm detects the optimal weights and structure of the MI-FUZWASDN using a power softplus activation function and while handling the model fitting and validation. Applications on two diagnostic breast cancer datasets validate and demonstrate the MI-FUZWASDN model’s exceptional learning and predicting performance. In addition, for the intrigued user, we have created a MATLAB kit, which is freely accessible via GitHub, to promote and support the results of this work.

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Availability of data and material

The data used in the paper entitled “A Fuzzy WASD Neuronet with Application in Breast Cancer Prediction” are taken from Kaggle in the following link: https://www.kaggle.com/.

Code availability

The complete development and implementation of the computational methods proposed in the paper entitled “A Fuzzy WASD Neuronet with Application in Breast Cancer Prediction” can be obtained from GitHub in the following link: https://github.com/SDMourtas/MI-FUZWASDN.

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Funding

No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. College of Applied Mathematics, Chengdu University of Information Technology, Chengdu, 610225, People’s Republic of China

    Theodore E. Simos

  2. Scientific and Educational Center “Digital Industry”, South Ural State University, 76 Lenin Ave., Chelyabinsk, Russia, 454 080

    Theodore E. Simos

  3. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City, 40402, Taiwan

    Theodore E. Simos

  4. Section of Mathematics, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Theodore E. Simos

  5. Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559, Athens, Greece

    Vasilios N. Katsikis & Spyridon D. Mourtas

  6. University of Athens Master of Business Administration, National and Kapodistrian University of Athens, Athens, Greece

    Vasilios N. Katsikis

Authors
  1. Theodore E. Simos
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  2. Vasilios N. Katsikis
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  3. Spyridon D. Mourtas
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Contributions

TES: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing-Original Draft. VNK: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing-Original Draft. SDM: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing-Original Draft.

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Correspondence to Vasilios N. Katsikis.

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Conflict of interest

The authors Theodore E. Simos, Vasilios N. Katsikis and Spyridon D. Mourtas of the paper entitled “A Fuzzy WASD Neuronet with Application in Breast Cancer Prediction” declare that there is no conflict of interest.

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Simos, T.E., Katsikis, V.N. & Mourtas, S.D. A fuzzy WASD neuronet with application in breast cancer prediction. Neural Comput & Applic 34, 3019–3031 (2022). https://doi.org/10.1007/s00521-021-06572-9

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  • DOI: https://doi.org/10.1007/s00521-021-06572-9

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