Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique.

The RFE function from the RFE class provided by the scikit-learn Python machine learning library has been employed here for implementation To explore the highly significant attributes that would yield the best performing accuracy when used in machine learning models, each type of feature selection approach selects the top 35, 30, 25, and 20 features from the PCOS dataset of 40 features.

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