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- W4313444487 abstract "Diabetes mellitus is the century’s epidemic. It is a metabolic disease and causes high blood sugar. Diabetes prediction is usually performed through collecting a blood sample and testing with some conditions. It is difficult to immediately predict the diabetic condition for the ICU admitted patients from the past history. In this work, we propose an ML technique that suits this data. The proposed approach was evaluated in the context of WIDS 2021 challenge using different classification techniques like logistic regression, random forest, K-nearest neighbors, and ensemble models like XGBoost and LightGBM, achieving one of the best results in the challenge. Obtained results are verified using receiver operating characteristic (ROC) curves in a systematic manner." @default.
- W4313444487 created "2023-01-06" @default.
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- W4313444487 date "2023-01-01" @default.
- W4313444487 modified "2023-10-18" @default.
- W4313444487 title "Comparative Analysis of Classification Methods to Predict Diabetes Mellitus on Noisy Data" @default.
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- W4313444487 doi "https://doi.org/10.1007/978-981-19-5868-7_23" @default.
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