AI model predicts prediabetes with high accuracy
A new AI model outperformed conventional machine learning models, offering a rapid, low-cost tool for early detection and targeted prevention.
Researchers have developed an AI model that enhances prediabetes prediction by integrating oxidative stress markers with traditional clinical indicators. The Pattern Neural Network model achieved 98.3% accuracy in Indian adults, outperforming other machine learning methods.
Total antioxidant status emerged as a key predictor, with lower antioxidant capacity observed in individuals with prediabetes. Waist circumference and BMI were also highly informative, alongside glucose markers such as HbA1c and OGTT.
The inclusion of oxidative stress measures provides a deeper understanding of the mechanisms underlying metabolic risk.
The study used clinical and biochemical data from 199 adults, with the PNN trained on 14 features, including demographic and biochemical variables. High accuracy across all sets indicates strong potential for quick, low-cost screening and personalised early interventions.
While the results are promising, the single-centre design and limited sample size indicate that external validation is needed. Future studies should test the model in larger, multi-site cohorts and integrate longitudinal data to enhance its real-world applicability and public health impact.
Would you like to learn more about AI, tech and digital diplomacy? If so, ask our Diplo chatbot!
