Machine Learning with Cardiotocography Data

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This study aims to enhance fetal monitoring by leveraging machine learning techniques to analyze CTG data. It employs a comprehensive ML pipeline featuring models like DNN, DRF, GBM, and K-Means, with the UCI CTG dataset for improving diagnostic accuracy. The research evaluates ensemble model accuracy, addresses challenges in the 'Suspect' class, and highlights advancements in model transparency. Results indicate variable model performance and underscore the importance of feature analysis and...