While AI models have been used for prediabetes prediction, most rely solely on standard clinical and biochemical markers. This study introduces a novel Pattern Neural Network (PNN) model that uniquely integrates total antioxidant scavenging potential with traditional risk factors, providing new insights into the role of oxidative stress in prediabetes risk stratification among Indian adults.
A total of 199 individuals aged 18 to 60 years were recruited and classified based on HbA1c levels into Control (n = 99) and Prediabetes (n = 100) groups. The PNN model achieved superior validation performance with an accuracy of 98.3%, outperforming SVM (96%), KNN (83%), and LR (71%). Notably, antioxidant scavenging potential and waist circumference emerged as the most influential predictors. The model’s output value of 0.8770 (threshold > 0.5) effectively identified individuals at increased risk of developing diabetes.
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