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- W4367011869 abstract "Nonparametric classification models are widely used in predictive analysis with multiple healthcare datasets. Even with high accuracy of different classifiers, we raise our concern that, in the field of health care, the cost of misclassification is supposed to be very high. Using maternal health risk data, the present paper proposes a complementary approach of combining parametric models along with sophisticated nonparametric classifiers to draw rich inferences about the causality of the associations between variables of importance. We present our case with the help of parametric model of binomial logistic regression. We clearly show how the inclusion of the parametric model improves the decision-making ability for better healthcare management." @default.
- W4367011869 created "2023-04-27" @default.
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- W4367011869 date "2023-01-01" @default.
- W4367011869 modified "2023-10-03" @default.
- W4367011869 title "Complementarity of Logistic Regression over the Nonparametric Classifications for Improved Decision-Making—A Case of Maternal Health Risk Data" @default.
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- W4367011869 doi "https://doi.org/10.1007/978-981-19-5191-6_8" @default.
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