Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks.
In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. We provide considerations useful for critically assessing the quality of the models and the results based on these models. Finally, we summarize our findings on how quality criteria for logistic regression and artificial neural network models are met in a sample of papers from the medical literature.
Artificial Neural Networks
Deep Learning Cookbook Douwe Osinga. Neuronal Dynamics Wulfram Gerstner. Artificial Intelligence Michael Negnevitsky. Deep Learning with Keras Sujit Pal.
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Learning TensorFlow Itay Lieder. The Book of Why Judea Pearl. Signals and Boundaries John H.
Elements of Causal Inference Bernhard Scholkopf. Python Deep Learning Gianmario Spacagna.
The Bayesian paradigm: second generation neural computing - Enlighten: Publications
Neural Networks with R Balaji Venkateswaran. Intelligence Emerging Keith L.
Deep Learning Neural Networks: Self-Organizing Maps Teuvo Kohonen. Deep Learning with Text Patrick Harrison. Other books in this series.
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- A review of evidence of health benefit from artificial neural networks in medical intervention?
- Artificial Neural Networks in Biomedicine : Piotr S. Szczepaniak : .
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Theory and Applications of Neural Networks J. Coupled Oscillating Neurons J. Neural Network Dynamics E.