Application of attention network test and demographic information to detect mild cognitive impairment via combining feature selection with support vector machine

BackgroundResearch into Alzheimer’s disease has shifted toward the identification of minimally invasive and less time-consuming modalities to define preclinical stages of Alzheimer’s disease.MethodHere, we propose visuomotor network dysfunctions as a potential biomarker in AD and its prodromal stage, mild cognitive impairment with underlying the Alzheimer’s disease pathology. The functionality of this network was tested in terms of timing, accuracy, and speed with goal-directed eye-hand tasks. The predictive power was determined by comparing the classification performance of a zero-rule algorithm (baseline), a decision tree, a support vector machine, and a neural network using functional parameters to classify controls without cognitive disorders, mild cognitive impaired patients, and Alzheimer’s disease patients.ResultsFair to good classification was achieved between controls and patients, controls and mild cognitive impaired patients, and between controls and Alzheimer’s disease patients with the support vector machine (77–82% accuracy, 57–93% sensitivity, 63–90% specificity, 0.74–0.78 area under the curve). Classification between mild cognitive impaired patients and Alzheimer’s disease patients was poor, as no algorithm outperformed the baseline (63% accuracy, 0% sensitivity, 100% specificity, 0.50 area under the curve).Comparison with Existing Method(s)The classification performance found in the present study is comparable to that of the existing CSF and MRI biomarkers.ConclusionThe data suggest that visuomotor network dysfunctions have potential in biomarker research and the proposed eye-hand tasks could add to existing tests to form a clear definition of the preclinical phenotype of AD.

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P1-311: Use of the support vector machine and sensitivity of an Alzheimer's disease-related region-of-interest gray matter classifier in identifying amnestic mild cognitive impairment subjects who convert to Alzheimer's disease: Preliminary findings from

Alzheimer s & Dementia ◽

10.1016/j.jalz.2008.05.902 ◽

2008 ◽

Vol 4 ◽

pp. T312-T312

Author(s):

Hang Zhang ◽

Tong Wu ◽

Min-Hyeok Bae ◽

Eric M. Reiman ◽

Gene E. Alexander ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Support Vector Machine ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Gray Matter ◽

Region Of Interest ◽

Amnestic Mild Cognitive Impairment ◽

Support Vector

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Individual Prediction of Cognitive Decline in Mild Cognitive Impairment Using Support Vector Machine-Based Analysis of Diffusion Tensor Imaging Data

Journal of Alzheimer s Disease ◽

10.3233/jad-2010-100840 ◽

2010 ◽

Vol 22 (1) ◽

pp. 315-327 ◽

Cited By ~ 61

Author(s):

Sven Haller ◽

Duy Nguyen ◽

Cristelle Rodriguez ◽

Joan Emch ◽

Gabriel Gold ◽

...

Keyword(s):

Support Vector Machine ◽

Cognitive Impairment ◽

Diffusion Tensor Imaging ◽

Mild Cognitive Impairment ◽

Cognitive Decline ◽

Diffusion Tensor ◽

Support Vector ◽

Imaging Data ◽

Diffusion Tensor Imaging Data

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An Embedded Feature Selection and Multi-Class Classification Method for Detection of the Progression from Mild Cognitive Impairment to Alzheimer's Disease

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.2888 ◽

2020 ◽

Vol 10 (2) ◽

pp. 370-379 ◽

Cited By ~ 1

Author(s):

Jie Cai ◽

Lingjing Hu ◽

Zhou Liu ◽

Ke Zhou ◽

Huailing Zhang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Selection ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Treatment Plan ◽

Feature Selection Method ◽

Selection Method ◽

Support Vector ◽

Feature Subset

Background: Mild cognitive impairment (MCI) patients are a high-risk group for Alzheimer's disease (AD). Each year, the diagnosed of 10–15% of MCI patients are converted to AD (MCI converters, MCI_C), while some MCI patients remain relatively stable, and unconverted (MCI stable, MCI_S). MCI patients are considered the most suitable population for early intervention treatment for dementia, and magnetic resonance imaging (MRI) is clinically the most recommended means of imaging examination. Therefore, using MRI image features to reliably predict the conversion from MCI to AD can help physicians carry out an effective treatment plan for patients in advance so to prevent or slow down the development of dementia. Methods: We proposed an embedded feature selection method based on the least squares loss function and within-class scatter to select the optimal feature subset. The optimal subsets of features were used for binary classification (AD, MCI_C, MCI_S, normal control (NC) in pairs) based on a support vector machine (SVM), and the optimal 3-class features were used for 3-class classification (AD, MCI_C, MCI_S, NC in triples) based on one-versus-one SVMs (OVOSVMs). To ensure the insensitivity of the results to the random train/test division, a 10-fold cross-validation has been repeated for each classification. Results: Using our method for feature selection, only 7 features were selected from the original 90 features. With using the optimal subset in the SVM, we classified MCI_C from MCI_S with an accuracy, sensitivity, and specificity of 71.17%, 68.33% and 73.97%, respectively. In comparison, in the 3-class classification (AD vs. MCI_C vs. MCI_S) with OVOSVMs, our method selected 24 features, and the classification accuracy was 81.9%. The feature selection results were verified to be identical to the conclusions of the clinical diagnosis. Our feature selection method achieved the best performance, comparing with the existing methods using lasso and fused lasso for feature selection. Conclusion: The results of this study demonstrate the potential of the proposed approach for predicting the conversion from MCI to AD by identifying the affected brain regions undergoing this conversion.

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Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment

Frontiers in Psychiatry ◽

10.3389/fpsyt.2018.00340 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 2

Author(s):

Xia-an Bi ◽

Qian Xu ◽

Xianhao Luo ◽

Qi Sun ◽

Zhigang Wang

Keyword(s):

Support Vector Machine ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Resting State ◽

Resting State Fmri ◽

Support Vector

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Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI

American Journal of Neuroradiology ◽

10.3174/ajnr.a3223 ◽

2012 ◽

Vol 34 (2) ◽

pp. 283-291 ◽

Cited By ~ 31

Author(s):

S. Haller ◽

P. Missonnier ◽

F.R. Herrmann ◽

C. Rodriguez ◽

M.-P. Deiber ◽

...

Keyword(s):

Support Vector Machine ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

White Matter ◽

Support Vector ◽

Machine Analysis

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A Role for Prior Knowledge in Statistical Classification of the Transition from Mild Cognitive Impairment to Alzheimer’s Disease

Journal of Alzheimer s Disease ◽

10.3233/jad-201398 ◽

2021 ◽

Vol 83 (4) ◽

pp. 1859-1875

Author(s):

Zihuan Liu ◽

Tapabrata Maiti ◽

Andrew R. Bender ◽

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Selection ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Support Vector ◽

Statistical Classification ◽

Relative Value ◽

Data Source ◽

Feature Selection Techniques

Background: The transition from mild cognitive impairment (MCI) to dementia is of great interest to clinical research on Alzheimer’s disease and related dementias. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), which often demonstrates classification accuracy equivalent or superior to other ML methods. Further, when faced with many potential features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different approaches for variable selection. Objective: The present study sought to compare different methods for statistical classification and for automated and theoretically guided feature selection techniques in the context of predicting conversion from MCI to dementia. Methods: We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to evaluate different influences of automated feature preselection on LR and support vector machine (SVM) classification methods, in classifying conversion from MCI to dementia. Results: The present findings demonstrate how similar performance can be achieved using user-guided, clinically informed pre-selection versus algorithmic feature selection techniques. Conclusion: These results show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM’s necessity or value for many clinical researchers.

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