An Explainable AI Techniques for Advancing Diabetes Prediction Using 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.