NextGenAI Genomic Biomarker System: A Hybrid Machine Learning Approach for Early Genetic Disorder Detection

Abstract: The interpretation of vast genomic datasets remains challenging due to complexity and cognitive burden on clinicians. The NextGen AI Genomic Biomarker System addresses these challenges through a hybrid architecture combining NLP and Deep Learning. The system leverages TF-IDF vectorization with Random Forest classification achieving weighted F1-score of 0.874, and employs CNN-LSTM architecture achieving AUC of 0.93. Integrated with SHAP-based explainability, the system provides transparent predictions with sub-2-second latency while maintaining HIPAA/GDPR compliance. Index Terms—Genomic biomarkers, precision medicine, NLP, deep learning, explainable AI, Random Forest, CNN-LSTM.