scholarly journals A MACHINE LEARNING FRAMEWORK FOR ASSESSMENT OF COGNITIVE AND FUNCTIONAL IMPAIRMENTS IN ALZHEIMER’S DISEASE: DATA PREPROCESSING AND ANALYSIS

2020 ◽  
pp. 1-8
Author(s):  
N. Vinutha ◽  
S. Pattar ◽  
S. Sharma ◽  
P. D. Shenoy ◽  
K.R. Venugopal
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Imbalanced Data ◽  
Functional Domain ◽  
Functional Impairments ◽  
Functional Activities ◽  
Learning Framework ◽  
Improved Performance ◽  
Aided Diagnosis

The neuropsychological scores and Functional Activities Questionnaire (FAQ) are significant to measure the cognitive and functional domain of the patients affected by the Alzheimer’s Disease. Further, there are standardized dataset available today that are curated from several centers across the globe that aid in development of Computer Aided Diagnosis tools. However, there are numerous clinical tests to measure these scores that lead to a challenging task for their assessment in diagnosis. Also, the datasets suffer from common missing and imbalanced data issues. In this paper, we propose a machine learning based framework to overcome these issues. Empirical results demonstrate that improved performance of Genetic Algorithm is obtained for the neuropsychological scores after Miss Forest Imputation and for FAQ scores is obtained after subjecting it to the Synthetic Minority Oversampling Technique.

scholarly journals Association Between Psychosis Phenotype and APOE Genotype on the Clinical Profiles of Alzheimer's Disease

2018 ◽  
Vol 15 (2) ◽  
pp. 187-194 ◽  
Author(s):  
Winnie Qian ◽  
Corinne E. Fischer ◽  
Tom A. Schweizer ◽  
David G. Munoz
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Apoe Genotype ◽  
Lewy Bodies ◽  
Functional Impairments ◽  
Significant Interaction Effect ◽  
Functional Activities ◽  
Apoe Ε4 ◽  
Lewy Body Pathology ◽  
Ε4 Allele

Background: Psychosis is a common phenomenon in Alzheimer's disease (AD). The APOE ε4 allele is the strongest genetic risk factor for the development of AD, but its association with psychosis remains unclear. Objective: We investigated the associations between psychosis, subdivided into delusions and hallucinations, as well as APOE ε4 allele on cognitive and functional outcomes. Secondarily, we investigated the associations between APOE ε4, Lewy bodies, and psychosis. Methods: Data from the National Alzheimer's Coordinating Center (NACC) were used. Nine hundred patients with a confirmed diagnosis of AD based on the NIA-AA Reagan were included in the analysis. Global cognition was assessed using the Mini-Mental State Exam (MMSE) and functional status was assessed using the Functional Activities Questionnaire (FAQ). Psychosis status was determined using the Neuropsychiatric Inventory Questionnaire (NPI-Q). Factorial design was used to assess the effects of psychosis and APOE ε4, as well as their interaction. Results: Psychosis and the presence of APOE ε4 were both associated with lower MMSE scores, while only psychosis was associated with higher FAQ scores. Furthermore, patients with hallucinations had lower MMSE and higher FAQ scores than patients with only delusions. There was a significant interaction effect between psychosis and APOE ε4 on MMSE scores, with APOE ε4 negatively affecting patients with hallucinations-only psychosis. APOE ε4 was positively associated with the presence of Lewy body pathology, and both were found to be more prevalent in psychotic patients, with a stronger association with hallucinations. Conclusion: Psychosis in AD was associated with greater cognitive and functional impairments. Patients with hallucinations-with or without delusions-conferred even greater deficits compared to patients with only delusions. The APOE ε4 allele was associated with worse cognition, especially for patients with hallucination-only psychosis. APOE ε4 may mediate cognitive impairment in the hallucinations phenotype through the development of Lewy bodies. Our findings support that subtypes of psychosis should be evaluated separately.

A Parallel Machine Learning Framework for Detecting Alzheimer’s Disease

2021 ◽  
pp. 423-432
Author(s):  
Sean A. Knox ◽  
Tianhua Chen ◽  
Pan Su ◽  
Grigoris Antoniou
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Parallel Machine ◽  
Learning Framework

scholarly journals Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer’s Disease

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Luís Costa ◽  
Miguel F. Gago ◽  
Darya Yelshyna ◽  
Jaime Ferreira ◽  
Hélder David Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Postural Control ◽  
Support Vector ◽  
Vector Machines ◽  
Postural Tasks ◽  
Mlp Classifier ◽  
Aided Diagnosis ◽  
Moca Score

The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer’s disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.

scholarly journals Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Steve Rodriguez ◽  
Clemens Hug ◽  
Petar Todorov ◽  
Nienke Moret ◽  
Sarah A. Boswell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Mechanisms ◽  
Drug Repurposing ◽  
Braak Stage ◽  
Learning Framework ◽  
Negative Results ◽  
Human Neural Cell ◽  
Ranked List

AbstractClinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization

2016 ◽  
Vol 13 (5) ◽  
pp. 498-508 ◽  
Author(s):  
V. Vigneron ◽  
A. Kodewitz ◽  
A. M. Tome ◽  
S. Lelandais ◽  
E. Lang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Support ◽  
Brain Areas

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-35 ◽  
Author(s):  
M. Tanveer ◽  
B. Richhariya ◽  
R. U. Khan ◽  
A. H. Rashid ◽  
P. Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques
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