Enhancing early-stage diabetes prediction with Explainable Artificial Intelligence(XAI)

This research explores predicting diabetes at an early stage using Explainable Artificial Intelligence (XAI) models. Precise forecasting is essential in diabetes prediction. XAI bridges complex models and practical insights. Machine learning models are applied for early-stage diabetes prediction based on personal health features. To make models easier to understand, we used a powerful technique called SHAP values, which reveals why models make certain predictions. This unlocks the decision-making process, showing how each feature influences predictions. Simple models like decision trees are naturally understandable, while complex models like Random Forest benefit from SHAP values to make them explainable. Analyzing these values not only identifies key features but also helps assess the fairness and reliability of models. The study emphasizes the trade-off between model complexity and transparency. It highlights the importance of explainability in diabetes prediction models and shows how SHAP values effectively unravel the mysteries of complex AI. As healthcare leans more on AI, this research aids in selecting better models and promoting reliable and transparent medical AI applications. By demystifying predictive models, it advances the discussion on reliable and effective AI use in crucial healthcare situations.