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This article presents a robust framework for predicting diabetes using an ensemble of various machine learning classifiers, addressing challenges such as outlier rejection and missing values. The proposed method outperforms existing techniques, achieving a maximum Area Under the ROC Curve (AUC) of 0.950, which is a 2% improvement over state-of-the-art results. The framework utilizes the Pima Indian Diabetes dataset and includes preprocessing steps like data standardization and feature selection, with the so

Author
anujpathak5735
Language
EN

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