Bio-AI Nexus: Integrating Biological Pathways with AI for Real-Time Metabolic Disorder Predictions

Abstract

The health risks related to metabolic disorders such as diabetes, obesity and cardiovascular diseases are global. Because conventional diagnostic methods involve periodic clinical assessment, diagnosis and treatment are often delayed. In this study, Artificial Intelligence (AI) and real-time prediction of metabolic disorders are integrated with biological pathways using genomic and metabolic pathway information together with clinical disease data. A CNN and LSTM deep learning model was developed to efficiently process clinical and biological data. It used 10,000 patient records drawn from electronic health records (EHRs), laboratory tests and biological pathway databases, split into training (70%), validation (15%) and testing (15%) sets. Normalization and feature engineering were applied as data preprocessing techniques to improve model performance. Compared with an XGBoost baseline, the CNN + LSTM model achieved an overall accuracy of 89.33%, superior recall (80% versus 77%) and ROC AUC (0.935 versus 0.91), making it the better model for estimating metabolic disorders. Further improvement is targeted towards enlarging the dataset, real-time clinical deployment and image data integration. Compared with manual approaches, this AI-driven approach improves early detection and intervention, aiming for better outcomes and a reduced healthcare burden.