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Title:Using generative AI to improve the performance and interpretability of rule-based diagnosis of Type 2 diabetes mellitus
Authors:ID Kopitar, Leon (Author)
ID Fister, Iztok (Author)
ID Štiglic, Gregor (Author)
Files:.pdf information-15-00162.pdf (1,29 MB)
MD5: 59EC8DBE817D570B9CA5890078BFB7D0
 
URL https://www.mdpi.com/2078-2489/15/3/162
 
Language:English
Work type:Scientific work
Typology:1.01 - Original Scientific Article
Organization:FZV - Faculty of Health Sciences
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy.
Keywords:GPT, association rule mining, classification, interpretability, diagnostics
Publication status:Published
Publication version:Version of Record
Submitted for review:09.02.2024
Article acceptance date:05.03.2024
Publication date:12.03.2024
Year of publishing:2024
Number of pages:str. 1-17
Numbering:letn. 15, št. 3, št. članka 162
PID:20.500.12556/DKUM-91187 New window
UDC:004.8:616.379-008.64
ISSN on article:2078-2489
COBISS.SI-ID:189955587 New window
DOI:10.3390/info15030162 New window
Publication date in DKUM:26.11.2024
Views:0
Downloads:6
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:P2-0057
Name:Obogatitev pogovornih razložljivih metod umetne inteligence v zdravstvu

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:12.03.2024

Secondary language

Language:Slovenian
Keywords:GPT, rudarjenje asociativnih pravil, klasifikacija, interpretacija, diagnostika


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