scholarly journals Predictive Models Based on Support Vector Machines: Whole-Brain versus Regional Analysis of Structural MRI in the Alzheimer's Disease

2014 ◽  
Vol 25 (4) ◽  
pp. 552-563 ◽  
Author(s):  
Alessandra Retico ◽  
Paolo Bosco ◽  
Piergiorgio Cerello ◽  
Elisa Fiorina ◽  
Andrea Chincarini ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machines ◽  
Predictive Models ◽  
Regional Analysis ◽  
Structural Mri ◽  
Support Vector ◽  
Whole Brain ◽  
Vector Machines

Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines

2009 ◽  
Vol 461 (1) ◽  
pp. 60-64 ◽  
Author(s):  
Diego Salas-Gonzalez ◽  
Juan M. Górriz ◽  
Javier Ramírez ◽  
Miriam López ◽  
Ignacio A. Illan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machines ◽  
Support Vector ◽  
Brain Images ◽  
Vector Machines

Classification for Alzheimer’s Disease from Structural MRI by General N-Dimensional Principal Component Analysis

2013 ◽  
Vol 336-338 ◽  
pp. 2316-2319
Author(s):  
Song Yuan Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Structural Mri ◽  
Component Analysis ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Normal Controls

In this paper, we propose a classification method for Alzheimer’s disease from structural MRI. In the method, a specific template is firstly constructed. Then all data are registered to the template and the corresponding Jacobians are calculated. And then, a general n-dimensional principal component analysis (GND-PCA) based method is adopted to extract features from the Jacobians and the features are enhanced by the linear discriminant analysis (LDA) . Finally, the enhanced features are used for the support vector machines (SVMs) classifiers. The proposed method classifies AD and normal controls (NC) well.

scholarly journals A systematic literature review of automatic Alzheimer’s disease detection from speech and language

2020 ◽  
Vol 27 (11) ◽  
pp. 1784-1797 ◽  
Author(s):  
Ulla Petti ◽  
Simon Baker ◽  
Anna Korhonen
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machines ◽  
Literature Review ◽  
Decision Trees ◽  
Systematic Literature Review ◽  
Neural Nets ◽  
Support Vector ◽  
Speech And Language ◽  
Vector Machines

Abstract Objective In recent years numerous studies have achieved promising results in Alzheimer’s Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. Materials and Methods We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? Results and Discussion We identified 33 eligible studies and 5 main findings: participants’ demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. Conclusion The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.

scholarly journals Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

2021 ◽  
Vol 25 (1) ◽  
pp. 218-226
Author(s):  
Chima S. Eke ◽  
Emmanuel Jammeh ◽  
Xinzhong Li ◽  
Camille Carroll ◽  
Stephen Pearson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Support Vector Machines ◽  
Early Detection ◽  
Blood Plasma ◽  
Plasma Proteins ◽  
Support Vector ◽  
Vector Machines ◽  
Blood Plasma Proteins

A Probe into the Parallel Elements of Diabetes Mellitus and Alzheimer’s Disease

2020 ◽  
Vol 17 (8) ◽  
pp. 3598-3604
Author(s):  
M. S. Roobini ◽  
M. Lakshmi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Morphological Changes ◽  
Memory Loss ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Huge Number ◽  
Learning Techniques ◽  
Vector Machines ◽  
The Mind

Alzheimer’s Disease (AD) is a standout amongst the most familiar types of memory loss influencing a huge number of senior individuals around the world which is the main source of dementia and memory misfortune. AD causes shrinkage in hippocampus and cerebral cortex and it grows the ventricles in the mind Enhancing home and network based composed consideration is basic to alleviating Alzheimer’s impacts on people and families and to decreasing mounting medicinal services costs. Distinguishing early morphological changes in the mind and making early determination are vital for Alzheimer’s ailment (AD). A few machine learning techniques, for example, Support vector machines have been utilized and a portion of these strategies have been appeared to be extremely compelling in diagnosing AD from neuroimages, some of the time significantly more viable than human radiologists. MRI uncover the data of AD however decay districts are diverse for various individuals which makes the finding somewhat trickier. By utilizing Convolutional Neural Networks, the issue can be settled with insignificant mistake rate. This paper proposes a profound Convolutional Neural Network (CNN) for Alzheimer’s Disease finding utilizing mind MRI information examination. The calculation was prepared and tried utilizing the MRI information from Alzheimer’s Disease Neuroimaging Initiative.

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