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@article{174243,
        author = {Fahmia Feroz and Sawaira Parviz and Zahoor Ahmad Qureshi},
        title = {Artificial intelligence in identifying diabetes onset},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3419-3424},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174243},
        abstract = {Diabetes is a prevalent metabolic disorder that necessitates early recognition to mitigate complications and enhance patient well-being. Emerging intelligent algorithms and computational learning models have revolutionized the identification of diabetes onset by analyzing intricate patterns within multimodal health data. Autonomous AI frameworks, leveraging deep neural architectures, decision forests, and adaptive learning systems, extract meaningful insights from biomarkers, genetic predispositions, lifestyle determinants, and longitudinal health records. Furthermore, cognitive computing integrated with real-time biosensing devices—such as continuous glucose monitors (CGMs) and smart wearables—enables dynamic risk profiling and proactive intervention strategies. This study explores the transformative role of AI in diabetes prognostication, emphasizing its predictive accuracy, self-evolving capabilities, and integration with big data ecosystems. By embedding AI-enhanced analytics into electronic health records (EHRs) and precision medicine paradigms, healthcare providers can shift from reactive treatment to anticipatory diabetes care, significantly alleviating the burden on global health systems.},
        keywords = {Autonomous AI frameworks, genetic predispositions, continuous glucose monitors (CGMs), self-evolving capabilities.},
        month = {March},
        }