research-article

Explanation-Driven HCI Model to Examine the Mini-Mental State for Alzheimer’s Disease

Authors:

Bhandari Mohan,

Bhadwal Singh Shikhar,

Mohammad Shorfuzzaman,

Mehedi MasudAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 2

Article No.: 41, Pages 1 - 16

https://doi.org/10.1145/3527174

Published: 26 September 2023 Publication History

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.

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Cited By

Mahmud TBarua KHabiba SSharmen NHossain MAndersson K(2024)An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer LearningDiagnostics10.3390/diagnostics1403034514:3(345)Online publication date: 5-Feb-2024
https://doi.org/10.3390/diagnostics14030345
Vimbi VShaffi NMahmud M(2024)Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detectionBrain Informatics10.1186/s40708-024-00222-111:1Online publication date: 5-Apr-2024
https://doi.org/10.1186/s40708-024-00222-1
Huang DGong LWei CWang XLiang Z(2024)An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue diseaseRespiratory Research10.1186/s12931-024-02874-325:1Online publication date: 18-Jun-2024
https://doi.org/10.1186/s12931-024-02874-3
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Index Terms

Explanation-Driven HCI Model to Examine the Mini-Mental State for Alzheimer’s Disease
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 2

February 2024

548 pages

EISSN:1551-6865

DOI:10.1145/3613570

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 26 September 2023

Online AM: 01 April 2022

Accepted: 14 March 2022

Revised: 12 February 2022

Received: 15 October 2021

Published in TOMM Volume 20, Issue 2

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Cited By

Mahmud TBarua KHabiba SSharmen NHossain MAndersson K(2024)An Explainable AI Paradigm for Alzheimer’s Diagnosis Using Deep Transfer LearningDiagnostics10.3390/diagnostics1403034514:3(345)Online publication date: 5-Feb-2024
https://doi.org/10.3390/diagnostics14030345
Vimbi VShaffi NMahmud M(2024)Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detectionBrain Informatics10.1186/s40708-024-00222-111:1Online publication date: 5-Apr-2024
https://doi.org/10.1186/s40708-024-00222-1
Huang DGong LWei CWang XLiang Z(2024)An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue diseaseRespiratory Research10.1186/s12931-024-02874-325:1Online publication date: 18-Jun-2024
https://doi.org/10.1186/s12931-024-02874-3
Vijayakumar SFlynn RCorcoran PMurray N(2024)The Role of ECG and Respiration in Predicting Quality of Experience2024 16th International Conference on Quality of Multimedia Experience (QoMEX)10.1109/QoMEX61742.2024.10598275(118-124)Online publication date: 18-Jun-2024
https://doi.org/10.1109/QoMEX61742.2024.10598275
Muhammad DBendechache M(2024)Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysisComputational and Structural Biotechnology Journal10.1016/j.csbj.2024.08.00524(542-560)Online publication date: Dec-2024
https://doi.org/10.1016/j.csbj.2024.08.005
Patankar MChaurasia VShandilya M(2024)A novel dense-net deep neural network with enhanced feature selection method for classification of different stages of tuberculosis using chest X-ray imagesMultimedia Tools and Applications10.1007/s11042-024-20381-xOnline publication date: 19-Oct-2024
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Khanal MKhadka SSubedi HChaulagain IRegmi LBhandari M(2023)Explaining the Factors Affecting Customer Satisfaction at the Fintech Firm F1 Soft by Using PCA and XAIFinTech10.3390/fintech20100062:1(70-84)Online publication date: 19-Jan-2023
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Bhandari MYogarajah PKavitha MCondell J(2023)Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAPApplied Sciences10.3390/app1305312513:5(3125)Online publication date: 28-Feb-2023
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McFall GBohn LGee MDrouin SFah HHan WLi LCamicioli RDixon R(2023)Identifying key multi-modal predictors of incipient dementia in Parkinson’s disease: a machine learning analysis and Tree SHAP interpretationFrontiers in Aging Neuroscience10.3389/fnagi.2023.112423215Online publication date: 30-Jun-2023
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Turcan GPeker S(2023)Profiling Individuals with Dementia Using Cluster Analysis2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10211598(1-7)Online publication date: 20-Jun-2023
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