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@article{193730,
        author = {A R Kishore Kumar and M.Uma Maheswari and K Sheshikala and S V Guru Pranathi and V Sri Siva Harinatha Reddy and D Varun Aditya},
        title = {FETAL HEALTH CLASSIFICATION USING OPTIMIZED ENSEMBLE MACHINE LEARNING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2127-2133},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193730},
        abstract = {Maternal and fetal mortality remain significant global healthcare challenges, necessitating precise fetal health assessment. While Cardiotocography (CTG) is a standard non-invasive method for monitoring fetal heart rate and uterine contractions, manual interpretation is often subjective and error-prone. This study proposes an automated classification system using ensemble machine learning techniques—specifically blending and stacking—to categorize fetal health into Normal, Suspect, and Pathological states. Results indicate that optimized ensemble models outperform individual classifiers, providing a reliable decision-support tool for early fetal distress detection.},
        keywords = {Cardiotocography (CTG), Fetal Health Prediction, Machine Learning, Ensemble Learning, Decision Support System, Healthcare AI},
        month = {March},
        }