| 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: |  Zlahtic-2023-Agile_Machine_Learning_Model_Deve.pdf (5,28 MB)
MD5: 97D6E8BCC8104EE31B189778BAA2D394
 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  |
|---|
| UDC: | 004.5 |
|---|
| ISSN on article: | 2076-3417 |
|---|
| COBISS.SI-ID: | 160320003 |
|---|
| DOI: | 10.3390/app13148329 |
|---|
| Publication date in DKUM: | 14.03.2024 |
|---|
| Views: | 299 |
|---|
| Downloads: | 36 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
