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@article{175857,
        author = {Karan Parge and Niraj Mahanta and Rajat Potgantiwar and Mayuresh Sontakke and Ankita Ghime},
        title = {ML-Driven Pregnancy Care: A Data Science Approach to Maternal Health},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4069-4074},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175857},
        abstract = {AI-powered systems, fueled by data science techniques, offer the ability to analyze diverse sets of health data from medical histories and lab results. By applying AI algorithms to this data, AI can predict potential pregnancy risks. Proposed data-driven approach bridges gaps in traditional maternal care, especially in remote or underserved areas where access to healthcare professionals may be limited. AI-powered pregnancy care represents a significant leap forward in maternal healthcare, offering innovative solutions for predicting risks, providing personalized care, and enhancing patient outcomes. The rise of AI provides a promising solution by offering automated, data-driven approaches for disease detection, leading to faster and more accurate diagnoses.AI serves as a powerful tool in the medical field, enabling the early and accurate detection of various diseases.},
        keywords = {maternal health, data analytics, AI, Data mining, Maternal Risk Factors},
        month = {April},
        }