1. Using generative AI to improve the performance and interpretability of rule-based diagnosis of Type 2 diabetes mellitusLeon Kopitar, Iztok Fister, Gregor Štiglic, 2024, original scientific article 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
Published in DKUM: 26.11.2024; Views: 0; Downloads: 6
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2. Time series numerical association rule mining variants in smart agricultureIztok Fister, Dušan Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz, 2023, original scientific article Keywords: association rule mining, smart agriculture, optimization, evolutionary algotihms, internet of things
Published in DKUM: 12.06.2024; Views: 121; Downloads: 9
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3. Action-Based Digital Characterization of a Game PlayerDamijan Novak, Domen Verber, Jani Dugonik, Iztok Fister, 2023, original scientific article Keywords: association rule mining, digital characterization, game agent, game player, real-time strategy games
Published in DKUM: 23.05.2024; Views: 131; Downloads: 7
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4. Variable-length differential evolution for numerical and discrete association rule miningUroš Mlakar, Iztok Fister, Iztok Fister, 2023, original scientific article Abstract: This paper proposes a variable-length Differential Evolution for Association Rule Mining. The proposed algorithm includes a novel representation of individuals, which can encode both numerical and discrete attributes in their original or absolute complement of the original intervals. The fitness function used is comprised of a weighted sum of Support and Confidence Association Rule Mining metrics. The proposed algorithm was tested on fourteen publicly available, and commonly used datasets from the UC Irvine Machine Learning Repository. It is also compared to the nature inspired algorithms taken from the NiaARM framework, providing superior results. The implementation of the proposed algorithm follows the principles of Green Artificial Intelligence, where a smaller computational load is required for obtaining promising results, and thus lowering the carbon footprint.
Keywords: association rule mining, differential evolution, data mining, variable-lenght solution representation, green AI
Published in DKUM: 18.01.2024; Views: 341; Downloads: 25
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5. uARMSolver: a framework for association rule miningIztok Fister, Iztok Fister, 2020, treatise, preliminary study, study Keywords: association rule mining, categorical attributes, numerical attributes, software framework, optimization
Published in DKUM: 17.03.2021; Views: 1446; Downloads: 41
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