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

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 r...

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Detalles Bibliográficos
Autores principales: Levman, Jacob, Ewenson, Bryan, Apaloo, Joe, Berger, Derek, Tyrrell, Pascal N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093437/
https://www.ncbi.nlm.nih.gov/pubmed/37046533
http://dx.doi.org/10.3390/diagnostics13071315
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author Levman, Jacob
Ewenson, Bryan
Apaloo, Joe
Berger, Derek
Tyrrell, Pascal N.
author_facet Levman, Jacob
Ewenson, Bryan
Apaloo, Joe
Berger, Derek
Tyrrell, Pascal N.
author_sort Levman, Jacob
collection PubMed
description 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 applications, which help illustrate the importance of producing reliable AI models. The validation software created is made publicly available, assisting anyone developing AI models for any supervised classification application in the creation of more reliable and predictable technologies.
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spelling pubmed-100934372023-04-13 Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics Levman, Jacob Ewenson, Bryan Apaloo, Joe Berger, Derek Tyrrell, Pascal N. Diagnostics (Basel) Article 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 applications, which help illustrate the importance of producing reliable AI models. The validation software created is made publicly available, assisting anyone developing AI models for any supervised classification application in the creation of more reliable and predictable technologies. MDPI 2023-04-01 /pmc/articles/PMC10093437/ /pubmed/37046533 http://dx.doi.org/10.3390/diagnostics13071315 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Levman, Jacob
Ewenson, Bryan
Apaloo, Joe
Berger, Derek
Tyrrell, Pascal N.
Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics
title Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics
title_full Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics
title_fullStr Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics
title_full_unstemmed Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics
title_short Error Consistency for Machine Learning Evaluation and Validation with Application to Biomedical Diagnostics
title_sort error consistency for machine learning evaluation and validation with application to biomedical diagnostics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093437/
https://www.ncbi.nlm.nih.gov/pubmed/37046533
http://dx.doi.org/10.3390/diagnostics13071315
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