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@article{179952,
        author = {Kunal Rajesh Parge and Pratiksha Sandip Jagtap and Tanishka Atul Pawar and Preeti Dnyanoba Kadam},
        title = {Machine Learning Driven Pregnancy Care System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8577-8584},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179952},
        abstract = {Machine learning (ML) techniques have 
revolutionized the healthcare domain by enabling the 
development of intelligent systems that can learn from 
health data and make accurate predictions. This paper 
presents an ML-driven pregnancy care system that 
analyzes key physiological parameters such as age, 
blood pressure, blood sugar, body temperature, heart 
rate and many more to predict potential pregnancy 
risks 
and detect diseases such as Anemia, 
Thrombocytopenia, Thalassemia, and Gestational 
Diabetes. The implemented system utilizes supervised 
learning algorithms like Random Forest and SVM for 
accurate classification of risk levels and disease status. 
In addition to prediction, the system offers trimester
wise personalized diet plans and disease-specific 
dietary 
and treatment recommendations. By 
integrating ML into maternal healthcare, this 
approach enhances early detection of complications, 
supports clinical decision- making, and promotes 
better outcomes for both mother and child—especially 
in areas with limited access to healthcare 
professionals. The system aims to deliver accessible, 
data-driven, and personalized care throughout 
pregnancy.},
        keywords = {maternal health, data analytics, AI,  Maternal Risk Factors},
        month = {May},
        }