Translating Genomic Variants Into Clinical Insights: An Advanced AI-Enabled Framework for Multi-Phenotype Disease Classification.

This research successfully demonstrated the development and deployment of a machine learning–based system for predicting genetic diseases directly from DNA variant data. By leveraging curated variant annotations, advanced feature engi-neering, and rigorous hyperparameter optimization, the model achieved high accuracy and robustness in classifying multiple clinically significant genetic disorders. The integration of the model into a user-friendly web application and REST API supports practical applicability in clinical and research envi-ronments, offering accessible decision support for genetic risk assessment.The system’s modular architecture and intuitive interface bridge the gap between complex genomic data and actionable insights, empowering users to understand their condition through clear explanations and to connect directly with local medical professionals for consultation. Despite promising results, future work will focus on expanding training with diverse real-world datasets, addressing rare variants, and incorporating explainable AI methods to further enhance clin-ical interpretability and trust. Overall, this work illustrates the transformative potential of combining machine learning with genomic information to accelerate precise disease diagnostics and advance personalized medicine. Continued innovation and integration will further solidify computational tools as indispensable assets in healthcare.

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