A fuzzy WASD neuronet with application in breast cancer prediction

• Published in: Neural computing & applications Vol. 34; no. 4; pp. 3019 - 3031
• Main Authors: Simos, Theodore E., Katsikis, Vasilios N., Mourtas, Spyridon D.
• Format: Journal Article
• Language: English
• Published: London Springer London 01-02-2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Back propagation; Breast cancer; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Fuzzy control; Fuzzy logic; Image Processing and Computer Vision; Original Article; Performance prediction; Probability and Statistics in Computer Science; Training; Breast cancer; Neural networks; Fuzzy logic system; WASD neuronet
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06572-9

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.