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Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics by Jacob Levman; Bryan Ewenson; Joe Apaloo; Derek Berger; Pascal N. Tyrrell is a Medicine article available to read on EtoBox.

What is Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics about?

Supervised machine learning classification is the most common example of artificial intelligence (AI) in industry and in academic research. These technologies predict whether a series of measurements belong to one of multiple groups of examples on which the machine was previously trained. Prior to real-world deployment, all implementations need to be carefully evaluated with hold-out validation, where the algorithm is tested on different samples than it was provided for training, in order to ensure the generalizability and reliability of AI models. However, established methods for performing hold-out validation do not assess the consistency of the mistakes that the AI model makes during hold-out validation. Here, we show that in addition to standard methods, an enhanced technique for performing hold-out validation—that also assesses the consistency of the sample-wise mistakes made by the learning algorithm—can assist in the evaluation and design of reliable and predictable AI models. The technique can be applied to the validation of any supervised learning classification application, and we demonstrate the use of the technique on a variety of example biomedical diagnostic applicati

Who reads Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics?

Author

Jacob Levman; Bryan Ewenson; Joe Apaloo; Derek Berger; Pascal N. Tyrrell

Publisher

MDPI AG

Published

2023

Language

EN

Field

Medicine (Health Sciences)