An Explainable AI Techniques for Advancing Diabetes Prediction Using Machine Learning
Classification algorithms; Diabetes prediction; Explainable AI; Feature selection; Machine learning
Researchers have developed an automated system to identify diabetes risk. This system combines data from two sources: a collection of female patients in Bangladesh and an expanded dataset from a local textile factory. The expanded dataset includes information from 203 additional patients. The system uses several techniques to improve its accuracy. It first identifies the most important factors for predicting diabetes, then employs a special model to estimate insulin levels. It also addresses challenges like imbalanced data (where one outcome is more common) and explains its predictions using artificial intelligence techniques. This system achieved the superlative results has an 81.0% accuracy rate, 0.812 F1 score, and 0.844 Area Under the Curve (AUC).. These metrics indicate strong performance in identifying diabetes risk. 2025 IEEE.
Saxena, Surabhi; Parveen, Nikhat; Yarlagadda, Bhanu Naga Karthik; Srujan, Appasani Siva Siva; Kumar, Mannava Ashok; Likhitha, Koka
2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.1-4
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/IC363308.2025.10956617 https://www.scopus.com/pages/publications/105003906581?origin=resultslist
Saxena S., Christ University, Department of Computer Science, Bangalore, India; Parveen N., College of Computing and Information Technology, University of Bisha, Department of Artificial Intelligence, Saudi Arabia; Yarlagadda B.N.K., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vijayawada, Guntur, India; Srujan A.S.S., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vijayawada, Guntur, India; Kumar M.A., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vijayawada, Guntur, India; Likhitha K., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vijayawada, Guntur, India