Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease

Zhang, Qian; Sheng, Jinhua; Zhang, Qiao; Wang, Luyun; Yang, Ze; Xin, Yu

doi:10.1016/j.compbiomed.2023.107392

Cited by 10 publications

(2 citation statements)

References 91 publications

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“…The presented pipeline provides a solution for creating large 3D tau inclusion density maps, co-registered to MRI images. Sheng et al [33] developed a hybrid multimodal machine-learning model for Alzheimer's disease detection. The aim is to improve AD diagnosis by combining various brain imaging data and CSF assays using advanced machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease

A.G,

2024

Preprint

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Alzheimer’s disease (AD) is a generic form of dementia causing memory loss and environmental responses. AD detection is pursued using the different protein structures and their intensity based on different physical behaviors. Using the time-series protein structures the detection and is eased through the proposed neural method for structural protein filling (NC-SSF). Structural differentiations are performed using the high and low intensity profiles observed. This analysis identifies the missing inputs and thereby the fillable sequences are identified. The protein biomarker determines the maximum filling requirement as per the changes observed. The neural network is trained using this sequence required under the low and high intensity variations. This process is recurrent until maximum false rate is confined through accuracy improvements. The AD progression detection is performed by estimating the intensity under different profile filling levels. The proposed method improves accuracy, sensitivity, and specificity by 8.74%, 10.29%, and 8.84% respectively. This method reduced the false rate and MMSE by 9.85% and 10.78% respectively.

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Section: Related Workmentioning

confidence: 99%

A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease

A.G,

2024

Preprint

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“…Furthermore, summarizing medical data can assist healthcare professionals in making accurate diagnoses and identifying trends in patient outcomes [ 46 ]. Fuzzy-based approaches are widely used in medical literature, especially in diagnosing [ 47 – 50 ] and detecting well-known diseases [ 51 – 54 ].…”

Section: Linguistic Summarization Techniquesmentioning

confidence: 99%

Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder

Öztürk,

Aydoğan,

Kök

et al. 2024

Health Inf Sci Syst

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Diagnosing autism spectrum disorder (ASD) in children poses significant challenges due to its complex nature and impact on social communication development. While numerous data analytics techniques have been proposed for ASD evaluation, the process remains time-consuming and lacks clarity. Eye tracking (ET) data has emerged as a valuable resource for ASD risk assessment, yet existing literature predominantly focuses on predictive methods rather than descriptive techniques that offer human-friendly insights. Interpretation of ET data and Bayley scales, a widely used assessment tool, is challenging for ASD assessment of children. It should be understood clearly to perform better analytic tasks on ASD screening. Therefore, this study addresses this gap by employing linguistic summarization techniques to generate easily understandable summaries from raw ET data and Bayley scales. By integrating ET data and Bayley scores, the study aims to improve the identification of children with ASD from typically developing children (TD). Notably, this research represents one of the pioneering efforts to linguistically summarize ET data alongside Bayley scales, presenting comparative results between children with ASD and TD. Through linguistic summarization, this study facilitates the creation of simple, natural language statements, offering a first and unique approach to enhance ASD screening and contribute to our understanding of neurodevelopmental disorders.

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A Novel Hybrid Algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation

Shen,

Wu,

et al. 2024

Water Resour Manage

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Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease

Cited by 10 publications

References 91 publications

A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease

A Predictive Sequence Filling Method using Protein Structures for Early Detection of Alzheimer Disease

Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder

A Novel Hybrid Algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation

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