Early Detection of the Alzheimer’s Disease: A Novel Cognitive Feature Selection Approach Using Machine Learning

Abstract Background An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease dementia. Methods We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer’s disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. Results Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. Conclusions Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

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Machine Learning Methods for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease. A Systematic Review.

10.21203/rs.3.rs-288564/v1 ◽

2021 ◽

Author(s):

Sergio Grueso ◽

Raquel Viejo-Sobera

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Detection ◽

Neurocognitive Disorders ◽

Multidimensional Data ◽

Support Vector

Abstract Background: Increase in life-span in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power, enable new methods for early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease.Methods: We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from Mild Cognitive Impairment to Alzheimer’s disease. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis. Results: Most studies used Magnetic Resonance Image and Positron Emission Tomography data but also Magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease Neuroimage Initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, biological, and behavioral) achieved the best performance. Conclusions: Although performance of the different models still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

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Machine learning methods with feature selection approach to estimate software services development effort

International Journal of Services Sciences ◽

10.1504/ijssci.2017.10009007 ◽

2017 ◽

Vol 6 (1) ◽

pp. 26

Author(s):

Amid Khatibi Bardsiri ◽

Seyyed Mohsen Hashemi

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Development Effort ◽

Learning Methods ◽

Machine Learning Methods ◽

Selection Approach ◽

Feature Selection Approach ◽

Software Services

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An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features

Applied Sciences ◽

10.3390/app10228093 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8093

Author(s):

Jun Wang ◽

Yuanyuan Xu ◽

Hengpeng Xu ◽

Zhe Sun ◽

Zhenglu Yang ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Learning Performance ◽

Space Structures ◽

Learning Tasks ◽

Feature Spaces ◽

Selection Approach ◽

Label Correlations ◽

Feature Selection Approach ◽

Low Dimensional

Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets. However, this strategy is weak in handling two kinds of features, that is, the irrelevant and redundant ones, which are collectively referred to as noisy features. These features may hamper the construction of optimal low-dimensional subspaces and compromise the learning performance of downstream tasks. In this study, we propose a novel multi-label feature selection approach by embedding label correlations (dubbed ELC) to address these issues. Particularly, we extract label correlations for reliable label space structures and employ them to steer feature selection. In this way, label and feature spaces can be expected to be consistent and noisy features can be effectively eliminated. An extensive experimental evaluation on public benchmarks validated the superiority of ELC.

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