scholarly journals A detailed investigation in determining Alzheimer’s disease and its risk factor using different classification techniques

2021 ◽  
Vol 12 (1) ◽  
pp. 374-377
Author(s):  
Mahendran Radha ◽  
Anitha M ◽  
Jeyabaskar Suganya
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Data Mining ◽  
Alzheimer's Disease ◽  
Genetic Disorders ◽  
Learning Approaches ◽  
Psychiatric Illnesses ◽  
Data Mining Approach ◽  
Diseased Person ◽  
Linear Svm

The prevalence of genetic disorders has recently crept surprisingly high. Neurodegenerative complications, specifically, pose physical and mental stress to parents and caretakers. These complications may be witnessed in the case of dementia. The general dementia type that accounted for between 60 to 80 per cent of psychiatric illnesses was Alzheimer's disease. At an earlier stage, illness detection serves as a critical task that helps the diseased person to enjoy a decent quality of life. It has become a much necessitated strategy towards relying on automated techniques like data mining approach for early diagnosis and assessment of risk factors concerned with Alzheimer’s. There has been an unprecedented growth of interest concerned with devising novelized approaches proposed in recent times for classifying the disease. However, there is still a grave need for developing an efficacious approach for better prognosis and classification. Data mining is carried out using different machine-learning approaches to assess the risk factors for Alzheimer's disease. Through the present research, and we compared numerous classification methods such as Decision Tree, Linear SVM, KNN, Logistic Regression, Radial SVM, and Random Forest, and finally reported the most outstanding approach in terms of its accuracy.

Elucidating the risk factors for progression from amyloid-negative amnestic mild cognitive impairment to dementia

2020 ◽  
Vol 17 ◽  
Author(s):  
Hyung-Ji Kim ◽  
Jae-Hong Lee ◽  
E-nae Cheong ◽  
Sung-Eun Chung ◽  
Sungyang Jo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Amnestic Mild Cognitive Impairment ◽  
Amyloid Pet ◽  
Clinical Progression ◽  
Amnestic Mci

Background: Amyloid PET allows for the assessment of amyloid β status in the brain, distinguishing true Alzheimer’s disease from Alzheimer’s disease-mimicking conditions. Around 15–20% of patients with clinically probable Alzheimer’s disease have been found to have no significant Alzheimer’s pathology on amyloid PET. However, a limited number of studies had been conducted this subpopulation in terms of clinical progression. Objective: We investigated the risk factors that could affect the progression to dementia in patients with amyloid-negative amnestic mild cognitive impairment (MCI). Methods: This study was a single-institutional, retrospective cohort study of patients over the age of 50 with amyloidnegative amnestic MCI who visited the memory clinic of Asan Medical Center with a follow-up period of more than 36 months. All participants underwent brain magnetic resonance imaging (MRI), detailed neuropsychological testing, and fluorine-18[F18]-florbetaben amyloid PET. Results: During the follow-up period, 39 of 107 patients progressed to dementia from amnestic MCI. In comparison with the stationary group, the progressed group had a more severe impairment in verbal and visual episodic memory function and hippocampal atrophy, which showed an Alzheimer’s disease-like pattern despite the lack of evidence for significant Alzheimer’s disease pathology. Voxel-based morphometric MRI analysis revealed that the progressed group had a reduced gray matter volume in the bilateral cerebellar cortices, right temporal cortex, and bilateral insular cortices. Conclusion: Considering the lack of evidence of amyloid pathology, clinical progression of these subpopulation may be caused by other neuropathologies such as TDP-43, abnormal tau or alpha synuclein that lead to neurodegeneration independent of amyloid-driven pathway. Further prospective studies incorporating biomarkers of Alzheimer’s diseasemimicking dementia are warranted.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

scholarly journals Examining the possible causal relationship between lung function, COPD and Alzheimer’s disease: a Mendelian randomisation study

2021 ◽  
Vol 8 (1) ◽  
pp. e000759
Author(s):  
Daniel Higbee ◽  
Raquel Granell ◽  
Esther Walton ◽  
Roxanna Korologou-Linden ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lung Function ◽  
Causal Relationship ◽  
Mendelian Randomisation ◽  
P Value ◽  
Unmeasured Confounding ◽  
Increased Risk ◽  
Shared Risk

RationaleLarge retrospective case-control studies have reported an association between chronic obstructive pulmonary disease (COPD), reduced lung function and an increased risk of Alzheimer’s disease. However, it remains unclear if these diseases are causally linked, or due to shared risk factors. Conventional observational epidemiology suffers from unmeasured confounding and reverse causation. Additional analyses addressing causality are required.ObjectivesTo examine a causal relationship between COPD, lung function and Alzheimer’s disease.MethodsUsing two-sample Mendelian randomisation, we used single nucleotide polymorphisms (SNPs) identified in a genome wide association study (GWAS) for lung function as instrumental variables (exposure). Additionally, we used SNPs discovered in a GWAS for COPD in those with moderate to very severe obstruction. The effect of these SNPs on Alzheimer’s disease (outcome) was taken from a GWAS based on a sample of 24 807 patients and 55 058 controls.ResultsWe found minimal evidence for an effect of either lung function (OR: 1.02 per SD; 95% CI 0.91 to 1.13; p value 0.68) or liability for COPD on Alzheimer’s disease (OR: 0.97 per SD; 95% CI 0.92 to 1.03; p value 0.40).ConclusionNeither reduced lung function nor liability COPD are likely to be causally associated with an increased risk of Alzheimer’s, any observed association is likely due to unmeasured confounding. Scientific attention and health prevention policy may be better focused on overlapping risk factors, rather than attempts to reduce risk of Alzheimer’s disease by targeting impaired lung function or COPD directly.

scholarly journals Physical Mistreatment in Persons with Alzheimer’s Disease

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Carla VandeWeerd ◽  
Gregory J. Paveza ◽  
Margaret Walsh ◽  
Jaime Corvin
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Conflict Resolution ◽  
Physical Violence ◽  
Emotional Health ◽  
Alcohol Problems ◽  
Community Based ◽  
Conflict Resolution Strategy ◽  
Resolution Strategy

Physical mistreatment has been estimated to affect 2 million older persons each year and dramatically affects health outcomes. While researchers have attempted to examine risk factors for specific forms of abuse, many have been able to focus on only victim or perpetrator characteristics, or a limited number of psychosocial variables at any one time. Additionally, data on risk factors for subgroups such as persons with Alzheimer’s disease who may have heightened and/or unique risk profiles has also been limited. This paper examines risk for physical violence in caregiver/patient dyads who participated in the Aggression and Violence in Community-Based Alzheimer’s Families Grant. Data were collected via in-person interview and mailed survey and included demographics as well as measures of violence, physical and emotional health, and health behaviors. Logistic regression analysis indicated that caregivers providing care to elders with high levels of functional impairment or dementia symptoms, or who had alcohol problems, were more likely to use violence as a conflict resolution strategy, as were caregivers who were providing care to elders who used violence against them. By contrast, caregivers with high self-esteem were less likely to use violence as a conflict resolution strategy. Significant interaction effects were also noted.

Sign in / Sign up

Export Citation Format

Share Document