Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence
Fuente:
arXiv
Guardado en:
| Autores principales: | Khokhar, Pir Bakhsh, Pentangelo, Viviana, Palomba, Fabio, Gravino, Carmine |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review
por: Khokhar, Pir Bakhsh, et al.
Publicado: (2024)
por: Khokhar, Pir Bakhsh, et al.
Publicado: (2024)
From Pixels to Explanations: Interpretable Diabetic Retinopathy Grading with CNN-Transformer Ensembles, Visual Explainability and Vision-Language Models
por: Khokhar, Pir Bakhsh, et al.
Publicado: (2026)
por: Khokhar, Pir Bakhsh, et al.
Publicado: (2026)
Transformer-Based Multi-Modal Temporal Embeddings for Explainable Metabolic Phenotyping in Type 1 Diabetes
por: Khokhar, Pir Bakhsh, et al.
Publicado: (2026)
por: Khokhar, Pir Bakhsh, et al.
Publicado: (2026)
RECOVER: Toward Requirements Generation from Stakeholders' Conversations
por: Voria, Gianmario, et al.
Publicado: (2024)
por: Voria, Gianmario, et al.
Publicado: (2024)
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements
por: Umm-e-Habiba, et al.
Publicado: (2022)
por: Umm-e-Habiba, et al.
Publicado: (2022)
On the Impact of 3D Visualization of Repository Metrics in Software Engineering Education
por: Di Dario, Dario, et al.
Publicado: (2024)
por: Di Dario, Dario, et al.
Publicado: (2024)
Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey
por: Arora, Lakshit, et al.
Publicado: (2025)
por: Arora, Lakshit, et al.
Publicado: (2025)
The Role of Artificial Intelligence and Machine Learning in Software Testing
por: Ramadan, Ahmed, et al.
Publicado: (2024)
por: Ramadan, Ahmed, et al.
Publicado: (2024)
The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence
por: White, Matt, et al.
Publicado: (2024)
por: White, Matt, et al.
Publicado: (2024)
Do Prompt Patterns Affect Code Quality? A First Empirical Assessment of ChatGPT-Generated Code
por: Della Porta, Antonio, et al.
Publicado: (2025)
por: Della Porta, Antonio, et al.
Publicado: (2025)
A Defect is Being Born: How Close Are We? A Time Sensitive Forecasting Approach
por: Robredo, Mikel, et al.
Publicado: (2026)
por: Robredo, Mikel, et al.
Publicado: (2026)
Towards MLOps: A DevOps Tools Recommender System for Machine Learning System
por: Shah, Pir Sami Ullah, et al.
Publicado: (2024)
por: Shah, Pir Sami Ullah, et al.
Publicado: (2024)
Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud Mitigation using Machine Learning Algorithms
por: Israfeel, Md
Publicado: (2026)
por: Israfeel, Md
Publicado: (2026)
A Catalog of Fairness-Aware Practices in Machine Learning Engineering
por: Voria, Gianmario, et al.
Publicado: (2024)
por: Voria, Gianmario, et al.
Publicado: (2024)
Predicting Open Source Software Sustainability with Deep Temporal Neural Hierarchical Architectures and Explainable AI
por: Karim, S M Rakib Ul, et al.
Publicado: (2026)
por: Karim, S M Rakib Ul, et al.
Publicado: (2026)
IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems
por: Speckmann, Pauline, et al.
Publicado: (2025)
por: Speckmann, Pauline, et al.
Publicado: (2025)
From Expectation to Habit: Why Do Software Practitioners Adopt Fairness Toolkits?
por: Voria, Gianmario, et al.
Publicado: (2024)
por: Voria, Gianmario, et al.
Publicado: (2024)
FedCSD: A Federated Learning Based Approach for Code-Smell Detection
por: Alawadi, Sadi, et al.
Publicado: (2023)
por: Alawadi, Sadi, et al.
Publicado: (2023)
Towards a Framework for Openness in Foundation Models: Proceedings from the Columbia Convening on Openness in Artificial Intelligence
por: Basdevant, Adrien, et al.
Publicado: (2024)
por: Basdevant, Adrien, et al.
Publicado: (2024)
Machine Learning Robustness: A Primer
por: Braiek, Houssem Ben, et al.
Publicado: (2024)
por: Braiek, Houssem Ben, et al.
Publicado: (2024)
Machine Learning with Requirements: a Manifesto
por: Giunchiglia, Eleonora, et al.
Publicado: (2023)
por: Giunchiglia, Eleonora, et al.
Publicado: (2023)
On The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios
por: Moussa, Rebecca, et al.
Publicado: (2022)
por: Moussa, Rebecca, et al.
Publicado: (2022)
A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research
por: Cao, Sicong, et al.
Publicado: (2024)
por: Cao, Sicong, et al.
Publicado: (2024)
Enhancing Formal Software Specification with Artificial Intelligence
por: Nassar, Antonio Abu, et al.
Publicado: (2026)
por: Nassar, Antonio Abu, et al.
Publicado: (2026)
Future of Artificial Intelligence in Agile Software Development
por: Mahboob, Mariyam, et al.
Publicado: (2024)
por: Mahboob, Mariyam, et al.
Publicado: (2024)
Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent
por: Shah, Mehil B, et al.
Publicado: (2025)
por: Shah, Mehil B, et al.
Publicado: (2025)
The Explabox: Model-Agnostic Machine Learning Transparency & Analysis
por: Robeer, Marcel, et al.
Publicado: (2024)
por: Robeer, Marcel, et al.
Publicado: (2024)
Stack Trace Deduplication: Faster, More Accurately, and in More Realistic Scenarios
por: Shibaev, Egor, et al.
Publicado: (2024)
por: Shibaev, Egor, et al.
Publicado: (2024)
GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks
por: Akmeemana, Lahiru, et al.
Publicado: (2025)
por: Akmeemana, Lahiru, et al.
Publicado: (2025)
Artificial Intelligence as a Catalyst for Innovation in Software Engineering
por: Fernández-y-Fernández, Carlos Alberto, et al.
Publicado: (2026)
por: Fernández-y-Fernández, Carlos Alberto, et al.
Publicado: (2026)
The Impact of Software Testing with Quantum Optimization Meets Machine Learning
por: Bandarupalli, Gopichand
Publicado: (2025)
por: Bandarupalli, Gopichand
Publicado: (2025)
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
por: Bouchoucha, Rached, et al.
Publicado: (2024)
por: Bouchoucha, Rached, et al.
Publicado: (2024)
asanAI: In-Browser, No-Code, Offline-First Machine Learning Toolkit
por: Koch, Norman, et al.
Publicado: (2025)
por: Koch, Norman, et al.
Publicado: (2025)
MicLog: Towards Accurate and Efficient LLM-based Log Parsing via Progressive Meta In-Context Learning
por: Yu, Jianbo, et al.
Publicado: (2026)
por: Yu, Jianbo, et al.
Publicado: (2026)
Machine Learning Systems: A Survey from a Data-Oriented Perspective
por: Cabrera, Christian, et al.
Publicado: (2023)
por: Cabrera, Christian, et al.
Publicado: (2023)
Model Lake: a New Alternative for Machine Learning Models Management and Governance
por: Garouani, Moncef, et al.
Publicado: (2025)
por: Garouani, Moncef, et al.
Publicado: (2025)
Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
por: Poenaru-Olaru, Lorena, et al.
Publicado: (2025)
por: Poenaru-Olaru, Lorena, et al.
Publicado: (2025)
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
por: Abdelkader, Hala, et al.
Publicado: (2024)
por: Abdelkader, Hala, et al.
Publicado: (2024)
Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
por: de Conto, Eduardo, et al.
Publicado: (2024)
por: de Conto, Eduardo, et al.
Publicado: (2024)
Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring
por: Moin, Armin, et al.
Publicado: (2022)
por: Moin, Armin, et al.
Publicado: (2022)
Ejemplares similares
-
Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review
por: Khokhar, Pir Bakhsh, et al.
Publicado: (2024) -
From Pixels to Explanations: Interpretable Diabetic Retinopathy Grading with CNN-Transformer Ensembles, Visual Explainability and Vision-Language Models
por: Khokhar, Pir Bakhsh, et al.
Publicado: (2026) -
Transformer-Based Multi-Modal Temporal Embeddings for Explainable Metabolic Phenotyping in Type 1 Diabetes
por: Khokhar, Pir Bakhsh, et al.
Publicado: (2026) -
RECOVER: Toward Requirements Generation from Stakeholders' Conversations
por: Voria, Gianmario, et al.
Publicado: (2024) -
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements
por: Umm-e-Habiba, et al.
Publicado: (2022)