Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

Naik, B. Balaji; Mehta, Ashir; Shah, Manan

doi:10.1186/s42492-020-00062-w

Cited by 29 publications

(7 citation statements)

References 110 publications

(113 reference statements)

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“…However, it was difficult to interpret the contributing features in deep learning models ( 27 ). Support vector machine (SVM) was a popular multivariate supervised data classification approach with performance being comparable or superior to other machine learning methods (e.g., k-nearest neighbor algorithm, Naive Bayes, decision trees, discriminant analysis), especially for small samples ( 39 ). The differentiation of EMCI from HC by using SVM further confirmed the clinical value of the information flow alterations in EMCI observed in this study.…”

Section: Discussionmentioning

confidence: 99%

Information Flow Pattern in Early Mild Cognitive Impairment Patients

Ding

Jiang

et al. 2021

Front. Neurol.

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Purpose: To investigate the brain information flow pattern in patients with early mild cognitive impairment (EMCI) and explore its potential ability of differentiation and prediction for EMCI.Methods: In this study, 49 patients with EMCI and 40 age- and sex-matched healthy controls (HCs) with available resting-state functional MRI images and neurological measures [including the neuropsychological evaluation and cerebrospinal fluid (CSF) biomarkers] were included from the Alzheimer's Disease Neuroimaging Initiative. Functional MRI measures including preferred information flow direction between brain regions and preferred information flow index of each brain region parcellated by the Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) were calculated by using non-parametric multiplicative regression-Granger causality analysis (NPMR-GCA). Edge- and node-wise Student's t-test was conducted for between-group comparison. Support vector classification was performed to differentiate EMCI from HC. The least absolute shrinkage and selection operator (lasso) regression were used to evaluate the predictive ability of information flow measures for the neurological state.Results: Compared to HC, disturbed preferred information flow directions between brain regions involving default mode network (DMN), executive control network (ECN), somatomotor network (SMN), and visual network (VN) were observed in patients with EMCI. An altered preferred information flow index in several brain regions (including the thalamus, posterior cingulate, and precentral gyrus) was also observed. Classification accuracy of 80% for differentiating patients with EMCI from HC was achieved by using the preferred information flow directions. The preferred information flow directions have a good ability to predict memory and executive function, level of amyloid β, tau protein, and phosphorylated tau protein with the high Pearson's correlation coefficients (r > 0.7) between predictive and actual neurological measures.Conclusion: Patients with EMCI were presented with a disturbed brain information flow pattern, which could help clinicians to identify patients with EMCI and assess their neurological state.

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Section: Discussionmentioning

confidence: 99%

Information Flow Pattern in Early Mild Cognitive Impairment Patients

Ding

Jiang

et al. 2021

Front. Neurol.

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“…Naik et al investigated the impact of multiple ML classifiers in MRI and the usage of SVM with various multimodal scans for identifying patients with AD/MCI against healthy controls. Findings were reached based on different classifier techniques and the presentation of the best multimodal paradigm for AD categorization [2]. Uysal and Ozturk used neuroimage analysis to detect dementia early in AD.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The disease's specific aetiology and treatment are yet unclear. Researchers have used a variety of neuroimaging methods, including single-photon emission computed tomography, magnetic resonance imaging (MRI), and positron emission tomography, to study AD [2]. In 2017, there were 121,404 deaths associated with AD, making it the sixth leading cause of death in the United States.…”

Section: Introductionmentioning

confidence: 99%

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer’s severity using multitask feature extraction

Ahmadi,

Javaheri,

Khajavi

et al. 2024

PLoS ONE

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Alzheimer’s disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer’s disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer’s disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer’s disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.

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“…ANN has several beneficial capabilities and properties such as adaption capability to sight changes in adjacent environment, and nonlinear fitness to underlying physical mechanisms and to provide details about the confidence in the decision made [35]. ANN is used to process nonlinear relationships between inputs and output, which can be used in character recognition, Stock Market prediction, Self Driving cars, Image Compression, and many more [36,37].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Patel

Shah

2021

SN COMPUT. SCI.

Self Cite

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Prediction of energy consumption and price is crucial in formatting policies related to the global energy market, demand, and supply. Data-driven analysis methods are giving rise to innovations in the world energy sector, including energy finance and economics. This paper has critically evaluated expand writings committed to Energy finance and economics applications of data-driven analysis. This paper comes up with an extensive view of state of the art in the area, which is already discussed with a different procedure. This review recognizes the applications of data-driven analysis methods in various areas such as forecasting domestic, nationwide, and transport energy consumption and price forecasting of multiple commodities, including crude oil, natural gas, and electricity. We have investigated certain research papers and given conclusion based on their researched and proposed model's prediction results and accuracies in respective areas. Our study suggests that Artificial Neural Network (ANN), Support Vector Machine (SVM), and other proposed neural network models are the most effective methods among other statistical and machine learning methods.

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Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

Cited by 29 publications

References 110 publications

Information Flow Pattern in Early Mild Cognitive Impairment Patients

Information Flow Pattern in Early Mild Cognitive Impairment Patients

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer’s severity using multitask feature extraction

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

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