Artificial Intelligence Techniques for the effective diagnosis of Alzheimer’s Disease: A Review

Aditya Shastry, K.; Sanjay, H. A.

doi:10.1007/s11042-023-16928-z

Cited by 6 publications

(1 citation statement)

References 128 publications

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“…This procedure, which depends on a limited amount of biologically annotated data, is expensive and time-consuming. Meta-learning accelerates this process by allowing users to interactively request new data points, enabling the creation of ML predictors of superior quality ( Aditya Shastry and Sanjay, 2023 ). AI and ML are transforming the field of drug development by employing a theoretical framework of graphical models to depict biological and chemical components.…”

mentioning

confidence: 99%

Letter to the Editor: “AI and ML in Alzheimer's disease: Transforming early detection and drug development”

Poongavanam,

Behera,

Dharmalingam Jothinathan

2024

Brain and Spine

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confidence: 99%

Letter to the Editor: “AI and ML in Alzheimer's disease: Transforming early detection and drug development”

Poongavanam,

Behera,

Dharmalingam Jothinathan

2024

Brain and Spine

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A novel approach to enhance feature selection using linearity assessment with ordinary least squares regression for Alzheimer’s Disease stage classification

Mabrouk,

Bouattour,

Mabrouki

et al. 2024

Multimed Tools Appl

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Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making

Albahri,

Hamid,

Alzubaidi

et al. 2024

Complex Intell. Syst.

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This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.

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Artificial Intelligence Techniques for the effective diagnosis of Alzheimer’s Disease: A Review

Cited by 6 publications

References 128 publications

Letter to the Editor: “AI and ML in Alzheimer's disease: Transforming early detection and drug development”

Letter to the Editor: “AI and ML in Alzheimer's disease: Transforming early detection and drug development”

A novel approach to enhance feature selection using linearity assessment with ordinary least squares regression for Alzheimer’s Disease stage classification

Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making

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