@article {26838,
	title = {A fuzzy WASD neuronet with application in breast cancer prediction},
	journal = {Neural Computing and Applications},
	year = {2021},
	month = {2021},
	abstract = {Cancer is one of the world{\textquoteright}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{\textquoteright}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.},
	isbn = {1433-3058},
	url = {https://doi.org/10.1007/s00521-021-06572-9},
	author = {Theodore E. Simos and Vasilios N. Katsikis and Spyridon D. Mourtas}
}
