A LIME-based approach for explainable AI in healthcare
Keywords:
Explainable Artificial Intelligence (XAI), AI, LIME, Diabetes Prediction, Multiclass Classification, Machine LearningAbstract
The current study focuses on the use of LIME in diabetes prediction as a multiclass classification problem, where patients are classified into three categories: No Diabetes, Pre-Diabetes, and Diabetes. The analysis demonstrates how LIME elucidates the importance of features such as Body Mass Index (BMI), age, physical health, and lifestyle factors in determining risk categories. By providing transparent explanations for predictions, LIME enhances trust in AI systems and supports medical practitioners in interpreting model outputs. Challenges in applying LIME to multiclass healthcare datasets, such as computational overhead and explanation reliability, are also discussed. This research underscores the role of LIME in enabling ethical, interpretable, and effective AI solutions for diabetes prediction.
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