• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• Korean Journal of Clinical Pharmacy
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• v.27 no.3
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• pp.136-142
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• 2017

Background: Dementia is one of important social and economic healthcare issues in the aging age. Therefore, it signifies to analyze the relationship between chronic disease or cardiovascular drug use and the incidence of dementia to establish a basis for increasing or preventing the risk of dementia. The purpose of this study was to investigate the correlation between the prevalence of chronic diseases and the use of cardiovascular drugs in patients diagnosed with dementia. Methods: In this study, we used data from sample of elderly patients from the Health Insurance Review and Assessment Service. We analyzed by logistic regression analysis with age, gender, and medication as covariates. KCD-7 was used to diagnosis of the disease, and drugs were analyzed using ATC codes and Korean standardized drug classification codes. Results: A total of 1,276,331 patients were analyzed in the sample of the elderly population, of which 532,075 (41.7%) were male and 744,256 (58.3%) were female. The patients have the higher risk of dementia in the older, women, and lower socioeconomically status. Cerebral infarction and ischemic heart disease increases risk of dementia. Patients taking statins, angiotensin converting enzyme inhibitor (ACEI) or angiotensin II receptor antagonists (ARB) showed low incidence of dementia. Conclusion: This study has been shown that ACEI, ARB, and statin drugs may associate with lower incidence of Alzheimer's and other dementia except vascular dementia.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.603-624
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• 2020

There is an emerging interest in brain functional connectivity (FC) based on functional Magnetic Resonance Imaging in Alzheimer's disease (AD) studies. The complex and high-dimensional structure of FC makes it challenging to explore the association between altered connectivity and AD susceptibility. We develop a pipeline to refine FC as proper covariates in a penalized logistic regression model and classify normal and AD susceptible groups. Three different quantification methods are proposed for FC refinement. One of the methods is dimension reduction based on common component analysis (CCA), which is employed to address the limitations of the other methods. We applied the proposed pipeline to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data and deduced pathogenic FC biomarkers associated with AD susceptibility. The refined FC biomarkers were related to brain regions for cognition, stimuli processing, and sensorimotor skills. We also demonstrated that a model using CCA performed better than others in terms of classification performance and goodness-of-fit.