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

Gaur, Loveleen; Bhandari, Mohan; Shikhar, Bhadwal Singh; Nz, Jhanjhi; Masud, Mehedi

doi:10.1145/3527174

Cited by 28 publications

(12 citation statements)

References 40 publications

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“…It may be good from an academic standpoint but contributes to added opaqueness in real-time. For instance, LIME and SHAP frameworks were used jointly in one study [113]. The feature rankings derived by these individual frameworks did not correlate with each other.…”

Section: Xai Researchers Often Resort To Self-intuition To De-mentioning

confidence: 99%

“…The feature rankings derived by these individual frameworks did not correlate with each other. The Mini-Mental State Examination (MMSE) significantly contributes to SHAP, whereas normalised Whole Brain Value (nWBV) dominates the LIME features [113].…”

Section: Xai Researchers Often Resort To Self-intuition To De-mentioning

confidence: 99%

“…The authors focused on using AI, HCI, and XAI in healthcare. There have been several noteworthy applications of XAI in AD classification too [113,114,115,116,117,118,119,120,121,122,123,124,125,126,127] However, an exclusive systematic review article on XAI for AD classification that points to various XAI frameworks and Blackbox models used inside these frameworks is yet to be proposed by the research community.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

Vimbi¹,

Shaffi²,

Mahmud

et al. 2023

Preprint

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Context: Artificial Intelligence (AI) in the medical domain has achieved remarkable results on various metrics primarily due to recent advancements in computational capabilities. However, AI models used for Computer Aided Diagnosis (CAD) have limited acceptance and trust due to their typical {Blackbox} nature. Consequently, explainable AI (XAI) is needed to justify the trustworthiness of the machine's predictions. Approach: In this work, we undertake a systematic review based on PRISMA guidelines on research articles investigating Alzheimer's Disease (AD) prediction with XAI for the past decade. The review was driven by carefully formulated research questions (RQ) which helped categorize AI models into different XAI methods(Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, etc) and frameworks (LIME, SHAP, GradCAM, LRP, etc.). Summary: This categorisation provides broad coverage of the interpretation spectrum from intrinsic (model-specific, ante-hoc models) to complex patterns (model-agnostic, post-hoc models) and by taking local explanations to a global scope. Additionally, the study categorizes merit in terms of different forms of interpretation to provide in-depth insight into the factors that support the clinical diagnosis of AD. Finally, limitations and open research challenges are outlined, and possible prospects are presented.

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Section: Xai Researchers Often Resort To Self-intuition To De-mentioning

confidence: 99%

Section: Xai Researchers Often Resort To Self-intuition To De-mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

Vimbi¹,

Shaffi²,

Mahmud

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Gaur et al [251] utilized XAI methods including LIME and SHAP in conjunction with machine learning algorithms including Logistic Regression(80.87%), Support Vector Machine(85.8%), K-nearest Neighbour(87.24%), Multilayer Perceptron(91.94%), and Decision Tree(100%) to build a robust explainable HCI model for examining the mini-mental state for Alzheimer's disease. It is worth mentioning that the most significant features contributing to the Alzheimer's disease examing were different for the LIME-based framework and the SHAP-based framework.…”

Section: ) Xai For Cyber Security Of Human-computer Interaction (Hci)mentioning

confidence: 99%

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

et al. 2022

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This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internetconnected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the ''black-box'' manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users' confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.

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“…Meanwhile, explainable artificial intelligence (XAI) [34] is a pragmatic tool that accelerates the creation of predictive models with domain knowledge and increases the transparency of automatically generated prediction models in the medical sector [35,36], which has assisted in providing results that are understandable to humans [37]. We noted that most of the recent research defined the detection of kidney problems in terms of individual categories, including stones [38], tumors [39], and cysts [40].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

et al. 2023

Self Cite

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Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model’s specific decisions and, thus, creating a “black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.

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

Cited by 28 publications

References 40 publications

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

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