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The researchers generated optimal fuzzy rules for diabetes prediction. The system showed 87.40% accuracy, 86.82% sensitivity, and 88% specificity. The finding demonstrated that the FP-TSK-FW is effective in the classification of diabetes. The results demonstrated 84% prediction accuracy.
Dec 3, 2021
Abstract: The use of methodologies based on machine learning is being increasingly used in health systems today, addressing different areas such as food, ...
Request PDF | On Jan 1, 2023, Alice Pintanel and others published Fuzzy Logic for Diabetes Predictions: A Literature Review | Find, read and cite all the ...
Thinking about it, in this work we present a systematic review of the literature with the objective of observing which strategies are currently being used to ...
The initial prediction stage adopts two computational intelligence and knowledge engineering techniques such as fuzzy logic (F), neural network (N) and case ...
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The objective of this study is to create a diabetes type 2 awareness model using a fuzzy logic machine learning tool.
Missing: Literature Review.
Apr 12, 2022 · In this paper, we have analysed the work done by various authors for diabetes prediction methods. Our analysis on diabetic prediction models was to find out ...
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Dec 4, 2023 · The diabetic prediction model achieved a remarkable accuracy of 97.40% through the utilization of PCA, logistic regression, and K-Means. However ...
Nov 16, 2021 · The paper proposes the integration of a type-2 fuzzy system and neural networks for the diagnosis of diabetes.
The second stage is implementation of fuzzy model for early prediction of type 2 diabetes. Predicted blood glucose values of proposal technique were compared ...