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- W4220965327 abstract "Directing research on Alzheimer’s disease toward only early prediction and accuracy cannot be considered a feasible approach toward tackling a ubiquitous degenerative disease today. Applying deep learning (DL), Explainable artificial intelligence, and advancing toward the human-computer interface (HCI) model can be a leap forward in medical research. This research aims to propose a robust explainable HCI model using SHAPley additive explanation, local interpretable model-agnostic explanations, and DL algorithms. The use of DL algorithms—logistic regression (80.87%), support vector machine (85.8%), k -nearest neighbor (87.24%), multilayer perceptron (91.94%), and decision tree (100%)—and explainability can help in exploring untapped avenues for research in medical sciences that can mold the future of HCI models. The presented model’s results show improved prediction accuracy by incorporating a user-friendly computer interface into decision-making, implying a high significance level in the context of biomedical and clinical research." @default.
- W4220965327 created "2022-04-03" @default.
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- W4220965327 date "2023-09-26" @default.
- W4220965327 modified "2023-10-16" @default.
- W4220965327 title "Explanation-driven HCI Model to Examine the Mini-Mental State for Alzheimer’s Disease" @default.
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- W4220965327 doi "https://doi.org/10.1145/3527174" @default.
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