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- W4289653514 abstract "Type-2 diabetes mellitus is a non-insulin dependent diabetes which accounts for 90–95% of the total population of diabetic patients. As the early stage detection is not diagnosed well, it may lead to detrimental consequences to the body organs. Recently many researchers have worked on Machine Learning-based techniques for diagnosis of such diseases. This work presents an improved Convolutional Neural Network, Functional Link Convolutional Neural Network (FLCNN) for the classification of Type-2 diabetes mellitus. The proposed technique is also compared with other Machine Learning models including Logistic Regression (LR), Support Vector Classification (SVC), K-Nearest Neighbors (KNN), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), XG Boost (XGB). Localized Diabetes Disease Dataset (LDD) was used, which was originally collected from Bombay Medical Hall, Ranchi, Jharkhand, India. Feature importance scores of different features are also presented. Among all the applied algorithms, the accuracy for FLCNN was found to be the highest, i.e., 90.45%." @default.
- W4289653514 created "2022-08-03" @default.
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- W4289653514 date "2022-08-04" @default.
- W4289653514 modified "2023-09-28" @default.
- W4289653514 title "An Improved Convolutional Neural Network for Classification of Type-2 Diabetes Mellitus" @default.
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- W4289653514 doi "https://doi.org/10.1007/978-981-19-2065-3_45" @default.
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