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@article{203045,
        author = {Dr.Madhu Gopinath and Aishwarya and Ansh and Chaitra and Chittaranjan Hosmani},
        title = {AI-Based Longitudinal Multi-Disease Maternal Health Monitoring and Risk Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {9654-9660},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203045},
        abstract = {Pregnancy health among mothers draws serious attention across the globe. When problems such as raised blood pressure, lacking iron, or glucose shifts appear, catching them fast improves safety. Modern systems support physicians by forecasting risks through advanced math models known as machine learning. Yet many current solutions track just single conditions - despite patients commonly dealing with overlapping concerns. Most of these systems pull data from patient histories and lab results to uncover patterns not obvious at first glance. Reviewing today’s technology reveals the types of algorithms applied, the sources of information used, along with their success rates in forecasting health risks. Yet upon deeper inspection, gaps remain - especially when it comes to projecting outcomes over time or tracking conditions spanning multiple pregnancies. What currently exists isn’t a finished solution, but rather a concept: one aiming to detect various complications simultaneously using linked components.
Maternal Health Predictions Using Machine Learning for Multiple Diseases},
        keywords = {},
        month = {May},
        }