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@article{160295,
        author = {Nanditha M and Sonia T and Apoorwa patil and Rachitha Shree R and Ms.P Sushmita Singh },
        title = {Fetal abnormalities prediction using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {1},
        pages = {291-295},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160295},
        abstract = {Fetal abnormalities pose significant risks to the health and well-being of both the mother and the unborn child. Early detection and accurate prediction of fetal abnormalities are crucial for timely interventions and personalized care. Machine learning techniques have emerged as a promising approach for predicting fetal abnormalities using various types of data, including ultrasound images, maternal biomarkers, and genetic information. This abstract presents a comprehensive overview of the research conducted on fetal abnormalities prediction using machine learning. The objective is to summarize the recent advancements, challenges, and potential solutions in this field.Several studies have demonstrated the efficacy of machine learning models in accurately identifying fetuses at high risk of developing abnormalities, surpassing the performance of traditional diagnostic methods. Key findings include the successful application of deep learning algorithms to analyze ultrasound images and detect fetal growth restriction and brain abnormalities. Additionally, machine learning models incorporating maternal biomarkers have shown promising results in predicting preterm birth and other fetal abnormalities. However, challenges such as data availability, potential bias, model interpretability, and the need for validation in real-world settings remain.},
        keywords = {Fetal abnormalities prediction using machine learning},
        month = {},
        }