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@article{198524,
        author = {Prof. Dr. Ram Kumar Solanki and Sachi Dugam and Bhavana Vuggina and Mehvish Shaikh and Bhagyesh Tiwari},
        title = {AI-Driven Predictive Analytics for Public Health},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11456-11461},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198524},
        abstract = {Predicting disease outbreaks and understanding population health trends have become vital in a world where new health threats can spread faster than ever. This paper presents an AI-powered public health analytics system that helps governments and health organizations stay one step ahead. The system uses machine learning to predict possible disease outbreaks, analyze vaccination effectiveness, and display real-time health insights through an interactive dashboard. By combining data from multiple sources such as epidemiological records, vaccination databases, and regional demographics the platform provides a complete picture of public health conditions. It highlights risk zones, tracks how well immunization programs are working, and helps decision-makers respond faster and smarter. The dashboard turns complex data into easy-to-read visuals, supporting quick and evidence-based actions. Over time, the system learns from new data, improving its accuracy and adapting to changing disease patterns. Together, these features make it a powerful step toward smarter, data-driven public health management and stronger global health preparedness.},
        keywords = {Predictive Analytics, Artificial Intelligence (AI), Public Health Informatics, Disease Outbreak Prediction, Vaccination Effectiveness, Epidemiological Modelling, Machine Learning, Health Data Visualization, Population Health Dashboard, Decision Support Systems.},
        month = {April},
        }