| Naslov: | Using generative AI to improve the performance and interpretability of rule-based diagnosis of Type 2 diabetes mellitus |
|---|
| Avtorji: | ID Kopitar, Leon (Avtor)
ID Fister, Iztok (Avtor)
ID Štiglic, Gregor (Avtor) |
|---|
| Datoteke: |  information-15-00162.pdf (1,29 MB)
MD5: 59EC8DBE817D570B9CA5890078BFB7D0
 https://www.mdpi.com/2078-2489/15/3/162
|
|---|
| Jezik: | Angleški jezik |
|---|
| Vrsta gradiva: | Znanstveno delo |
|---|
| Tipologija: | 1.01 - Izvirni znanstveni članek |
|---|
| Organizacija: | FZV - Fakulteta za zdravstvene vede
FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
|
|---|
| Opis: | 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.
|
|---|
| Ključne besede: | GPT, association rule mining, classification, interpretability, diagnostics |
|---|
| Status publikacije: | Objavljeno |
|---|
| Verzija publikacije: | Objavljena publikacija |
|---|
| Poslano v recenzijo: | 09.02.2024 |
|---|
| Datum sprejetja članka: | 05.03.2024 |
|---|
| Datum objave: | 12.03.2024 |
|---|
| Založnik: | MDPI |
|---|
| Leto izida: | 2024 |
|---|
| Št. strani: | str. 1-17 |
|---|
| Številčenje: | letn. 15, št. 3, št. članka 162 |
|---|
| PID: | 20.500.12556/DKUM-91187  |
|---|
| UDK: | 004.8:616.379-008.64 |
|---|
| COBISS.SI-ID: | 189955587 |
|---|
| DOI: | 10.3390/info15030162 |
|---|
| ISSN pri članku: | 2078-2489 |
|---|
| Avtorske pravice: | © 2024 by the authors. |
|---|
| Datum objave v DKUM: | 26.11.2024 |
|---|
| Število ogledov: | 0 |
|---|
| Število prenosov: | 250 |
|---|
| Metapodatki: |    |
|---|
| Področja: | Ostalo
|
|---|
|
:
|
Kopiraj citat |
|---|
| | | | Skupna ocena: | (0 glasov) |
|---|
| Vaša ocena: | Ocenjevanje je dovoljeno samo prijavljenim uporabnikom. |
|---|
| Objavi na: |  |
|---|
Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše
podrobnosti ali sproži prenos. |
