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    Diabetes Prediction Using Derived Features and Ensembling of Boosting Classifiers

    R. Rajkamal1,*, Anitha Karthi2, Xiao-Zhi Gao3

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 2013-2033, 2022, DOI:10.32604/cmc.2022.027142

    Abstract Diabetes is increasing commonly in people’s daily life and represents an extraordinary threat to human well-being. Machine Learning (ML) in the healthcare industry has recently made headlines. Several ML models are developed around different datasets for diabetic prediction. It is essential for ML models to predict diabetes accurately. Highly informative features of the dataset are vital to determine the capability factors of the model in the prediction of diabetes. Feature engineering (FE) is the way of taking forward in yielding highly informative features. Pima Indian Diabetes Dataset (PIDD) is used in this work, and the… More >

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