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Segregation models aid healthcare diagnosis, reducing errors. Extracting real-world medical data is challenging due to variability. This study automates distinguishing cardiotocographic regions in pregnancy using the Random Forest method with 94% accuracy. Comparisons with other methods are detailed in the research paper.
Jeet-51/Prediction-System-Design-for-Monitoring-the-Health-of-Developing-Infants-using-Statistical-ML
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Segregation models aid healthcare diagnosis, reducing errors. Extracting real-world medical data is challenging due to variability. This study automates distinguishing cardiotocographic regions in pregnancy using the Random Forest method with 94% accuracy. Comparisons with other methods are detailed in the research paper.
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