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Zhang Q, Sheng J, Zhang Q, Wang L, Yang Z, Xin Y. Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease. Comput Biol Med 2023; 165:107392. [PMID: 37669585 DOI: 10.1016/j.compbiomed.2023.107392] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/30/2023] [Accepted: 08/25/2023] [Indexed: 09/07/2023]
Abstract
In order to stop deterioration and give patients with Alzheimer's disease (AD) early therapy, it is crucial to correctly diagnose AD and its early stage, mild cognitive impairment (MCI). A framework for diagnosing AD is presented in this paper, which includes magnetic resonance imaging (MRI) image preprocessing, feature extraction, and the Fuzzy k-nearest neighbor algorithm (FKNN) model. In particular, the framework's novelty lies in the use of an improved Harris Hawks Optimization (HHO) algorithm named SSFSHHO, which integrates the Sobol sequence and Stochastic Fractal Search (SFS) mechanisms for optimizing the parameters of FKNN. The HHO method improves the quality of the initial population overall by incorporating the Sobol sequence, and the SFS mechanism increases the algorithm's capacity to get out of the local optimum solution. Comparisons with other classical meta-heuristic algorithms, state-of-the-art HHO variants in low and high dimensions, and enhanced meta-heuristic algorithms on 30 typical IEEE CEC2014 benchmark test problems show that the overall performance of SSFSHHO is significantly better than other comparative algorithms. Moreover, the created framework based on the SSFSHHO-FKNN model is employed to classify AD and MCI using MRI scans from the ADNI dataset, achieving high classification performance for 6 representative cases. Experimental findings indicate that the proposed algorithm performs better than a number of high-performance optimization algorithms and classical machine learning algorithms, thus offering a promising approach for AD classification. Additionally, the proposed strategy can successfully identify relevant features and enhance classification performance for AD diagnosis.
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Affiliation(s)
- Qian Zhang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China; School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, Zhejiang, 325035, China
| | - Jinhua Sheng
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China.
| | - Qiao Zhang
- Beijing Hospital, Beijing, 100730, China; National Center of Gerontology, Beijing, 100730, China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Luyun Wang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Ze Yang
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Yu Xin
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
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Altered time-varying local spontaneous brain activity pattern in patients with high myopia: a dynamic amplitude of low-frequency fluctuations study. Neuroradiology 2023; 65:157-166. [PMID: 35953566 DOI: 10.1007/s00234-022-03033-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 07/29/2022] [Indexed: 01/10/2023]
Abstract
PURPOSE To investigate the abnormal time-varying local spontaneous brain activity in patients with high myopia (HM) on the basis of the dynamic amplitude of low-frequency fluctuations (dALFF) approach. METHODS Age and gender matching were performed based on resting-state functional magnetic resonance imaging data from 86 HM patients and 87 healthy controls (HCs). Local spontaneous brain activities were evaluated using the time-varying dALFF method. Support vector machine combined with the radial basis function kernel was used for pattern classification analysis. RESULTS Inter-group comparison between HCs and HM patients has demonstrated that dALFF variability in the left inferior frontal gyrus (orbital part), left lingual gyrus, right anterior cingulate and paracingulate gyri, and right calcarine fissure and surrounding cortex was decreased in HM patients, while increased in the left thalamus, left paracentral lobule, and left inferior parietal (except supramarginal and angular gyri). Pattern classification between HM patients and HCs displayed a classification accuracy of 85.5%. CONCLUSION In this study, the findings mentioned above have suggested the association between local brain activities of HM patients and abnormal variability in brain regions performing visual sensorimotor and attentional control functions. Several useful information has been provided to elucidate the mechanism-related alterations of the myopic nervous system. In addition, the significant role of abnormal dALFF variability has been highlighted to achieve an in-depth comprehension of the pathological alterations and neuroimaging mechanisms in the field of HM.
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Zhang X, Liu J, Chen Y, Jin Y, Cheng J. Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly-available nodes approach. Brain Behav 2021; 11:e02027. [PMID: 33393200 PMCID: PMC7994705 DOI: 10.1002/brb3.2027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 12/01/2020] [Accepted: 12/21/2020] [Indexed: 01/22/2023] Open
Abstract
INTRODUCTION Using brain network and graph theory methods to analyze the Alzheimer's disease (AD) and mild cognitive impairment (MCI) abnormal brain function is more and more popular. Plenty of potential methods have been proposed, but the representative signal of each brain region in these methods remains poor performance. METHODS We propose a highly-available nodes approach for constructing brain network of patients with MCI and AD. With resting-state functional magnetic resonance imaging (rs-fMRI) data of 84 AD subjects, 81 MCI subjects, and 82 normal control (NC) subjects from the Alzheimer's Disease Neuroimaging Initiative Database, we construct connected weighted brain networks based on the different sparsity and minimum spanning tree. Support Vector Machine of Radial Basis Function kernel was selected as classifier. RESULTS Accuracies of 74.09% and 77.58% in classification of MCI and AD from NC, respectively. We also performed a hub node analysis and found 18 significant brain regions were identified as hub nodes. CONCLUSIONS The findings of this study provide insights for helping understanding the progress of the AD. The proposed method highlights the effectively representative time series of brain regions of rs-fMRI data for construction and topology analysis brain network.
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Affiliation(s)
- Xiaopan Zhang
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Junhong Liu
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Yuan Chen
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Yanan Jin
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
| | - Jingliang Cheng
- Department of Magnetic Resonance ImagingThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouChina
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