Diaproteo: A supervised learning framework for early detection of diabetes mellitus based on proteomic profiles

Diabetes mellitus (DM) is a chronic metabolic disease that affects millions of people worldwide, posing major health risks and financial challenges. Early diagnosis and treatment are essential for reducing complications and improving patient outcomes. This research explores the application of supervised algorithms to predict DM using a variety of datasets such as clinical features, genetic markers, and lifestyle variables. This study proposes novel approaches and evaluates prediction models with classic machine learning algorithms and cutting-edge deep learning architecture. Performance metrics (accuracy, precision, recall, F1 score) reveal that the Extra Trees model for the independent test and Convolutional Neural Network (CNN) for 10-fold cross-validation, achieving 91.52% accuracy with an F1 score of 0.91 (Extra Trees) and 87.03% accuracy with an F1 score of 84.82% (CNN). In addition, other evaluation indicators demonstrated that the Extra Trees algorithm outperformed others, achieving the highest accuracy on the independent test. Our study shows that machine learning and deep learning approaches may accurately predict DM, demonstrating the potential for early intervention and personalized healthcare strategies.