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Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction...
This paper deals with the comparison of the two neural network methods of learning: supervised (classical feedforward neural networks: multi-layer neural networks (MLP), radial basis function (RBF) and probabilistic neural networks (PNN)) and unsupervised (self organizing feature maps (SOFM), or Kohonen map), in order to assess their performances on a labeled breast cancer database. By revealing their...
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