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Title:Agile Machine Learning Model Development Using Data Canyons in Medicine : A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement
Authors:ID Žlahtič, Bojan (Author)
ID Završnik, Jernej (Author)
ID Blažun Vošner, Helena (Author)
ID Kokol, Peter (Author)
ID Šuran, David (Author)
ID Završnik, Tadej (Author)
Files:.pdf Zlahtic-2023-Agile_Machine_Learning_Model_Deve.pdf (5,28 MB)
MD5: 97D6E8BCC8104EE31B189778BAA2D394
 
URL https://www.mdpi.com/2076-3417/13/14/8329
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
FNM - Faculty of Natural Sciences and Mathematics
Abstract:Over the past few decades, machine learning has emerged as a valuable tool in the field of medicine, driven by the accumulation of vast amounts of medical data and the imperative to harness this data for the betterment of humanity. However, many of the prevailing machine learning algorithms in use today are characterized as black-box models, lacking transparency in their decision-making processes and are often devoid of clear visualization capabilities. The transparency of these machine learning models impedes medical experts from effectively leveraging them due to the high-stakes nature of their decisions. Consequently, the need for explainable artificial intelligence (XAI) that aims to address the demand for transparency in the decision-making mechanisms of black-box algorithms has arisen. Alternatively, employing white-box algorithms can empower medical experts by allowing them to contribute their knowledge to the decision-making process and obtain a clear and transparent output. This approach offers an opportunity to personalize machine learning models through an agile process. A novel white-box machine learning algorithm known as Data canyons was employed as a transparent and robust foundation for the proposed solution. By providing medical experts with a web framework where their expertise is transferred to a machine learning model and enabling the utilization of this process in an agile manner, a symbiotic relationship is fostered between the domains of medical expertise and machine learning. The flexibility to manipulate the output machine learning model and visually validate it, even without expertise in machine learning, establishes a crucial link between these two expert domains.
Keywords:XAI, explainable artificial intelligence, data canyons, machine learning, transparency, agile development, white-box model
Publication status:Published
Publication version:Version of Record
Submitted for review:03.07.2023
Article acceptance date:17.07.2023
Publication date:19.07.2023
Publisher:MDPI
Year of publishing:2023
Number of pages:Str. 1-12
Numbering:Letn. 13, št. 14, št. članka 8329
PID:20.500.12556/DKUM-87387 New window
UDC:004.5
ISSN on article:2076-3417
COBISS.SI-ID:160320003 New window
DOI:10.3390/app13148329 New window
Publication date in DKUM:14.03.2024
Views:299
Downloads:36
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
ŽLAHTIČ, Bojan, ZAVRŠNIK, Jernej, BLAŽUN VOŠNER, Helena, KOKOL, Peter, ŠURAN, David and ZAVRŠNIK, Tadej, 2023, Agile Machine Learning Model Development Using Data Canyons in Medicine : A Step towards Explainable Artificial Intelligence and Flexible Expert-Based Model Improvement. Applied sciences [online]. 2023. Vol. 13, no. 14,  članka 8329, p. 1–12. [Accessed 26 April 2025]. DOI 10.3390/app13148329. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=87387
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Record is a part of a journal

Title:Applied sciences
Shortened title:Appl. sci.
Publisher:MDPI
ISSN:2076-3417
COBISS.SI-ID:522979353 New window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:19.07.2023

Secondary language

Language:Slovenian
Keywords:umetna inteligenca, podatkovni kanjoni, strojno učenje, preglednost, agilni razvoj, model bele škatle


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