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@article{200516,
        author = {Shekhar Chaudhary and Sunder Kumar and Deepak Kumar and Himani Rawat and Muskan khan},
        title = {Medi Predict AI – Integrated Multi-Disease Risk Assessment System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2622-2624},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200516},
        abstract = {I am currently working on a project that addresses the growing need for intelligent, automated healthcare diagnostic systems capable of assessing multiple disease risks in an integrated manner. Traditional diagnostic tools often operate in isolation, focusing on single diseases and relying heavily on manual interpretation, which can lead to delays, inconsistencies, and missed early warnings. As healthcare data volumes grow—including patient history, symptoms, lab results, and imaging data—the complexity of accurate Multidisease risk prediction increases significantly. This research presents MediPredict AI, a unified, AI-driven risk assessment system that Analyzes heterogeneous health data to provide early, explainable predictions for multiple diseases simultaneously. The system extracts features from structured and unstructured medical inputs, applies hybrid machine learning models, and delivers risk scores with clinical reasoning. Developed as a web application with a .NET backend, MediPredict AI integrates with electronic health records (EHRs) and supports real-time analytics. Preliminary validation using public health datasets shows promising accuracy in predicting risks for cardiovascular, diabetic, and respiratory conditions, with improved diagnostic speed and clinician workload reduction.},
        keywords = {},
        month = {May},
        }