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- W4367011705 abstract "Even with multi-fold growth in medical diagnostics, continuous and error-free monitoring of pregnant women during the gestational stage has attracted many eye-catching concerns, globally. These diagnostics play a vital role in the overall assessment of fetal and expecting women’s health. Cardiotocography is one such technique that acquires sophisticated information of fetal heart to access the fetus’s health status. However, because of human interventions, it is also prone to errors. Therefore, the present work proposes a machine learning-based prognosis tool that will assist the medical practitioners for the early and critical assessment of fetal health. For this purpose, Random Forest, k-Nearest Neighbor, Logistic Regression, Gradient Boost, and Extreme Gradient Boost techniques are employed. Also, the grid search cross-validation is utilized to estimate the optimum hyperparameters of these models. The results reveal that the Extreme Gradient Boost algorithm performs remarkably and achieved an accuracy of greater than 94% that is significantly better than all other models." @default.
- W4367011705 created "2023-04-27" @default.
- W4367011705 creator A5022548683 @default.
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- W4367011705 date "2023-01-01" @default.
- W4367011705 modified "2023-09-25" @default.
- W4367011705 title "A Machine Learning-Based Prediction Model for Fetal Health Assessment" @default.
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- W4367011705 doi "https://doi.org/10.1007/978-981-19-5191-6_20" @default.
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