Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

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Mild cognitive impairment understanding: an empirical study by data-driven approach

BMC Bioinformatics ◽

10.1186/s12859-019-3057-1 ◽

2019 ◽

Vol 20 (S15) ◽

Author(s):

Liyuan Liu ◽

Bingchen Yu ◽

Meng Han ◽

Shanshan Yuan ◽

Na Wang

Keyword(s):

Risk Factors ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Sleep Time ◽

Data Driven ◽

Control And Prevention ◽

Data Driven Approach ◽

Personal Welfare ◽

First Time

Abstract Background Cognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline/impairment (MCI) can further develop into Dementia/Alzheimer’s disease. While treatment of Dementia/Alzheimer’s disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy. Results In this study, based on the data collected by Centers for Disease Control and Prevention (CDC) through the nationwide telephone survey, we apply a data-driven approach to re-exam the previously founded risk factors and discover new risk factors. We found that depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery. Besides that, the first time, we point out that other factors such as arthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is less exploited previously. We also incorporate some machine learning and deep learning algorithms to weigh the importance of various factors contributed to MCI and predicted cognitive declined. Conclusion By incorporating the data-driven approach, we can determine that risk factors significantly correlated with diseases. These correlations could also be expanded to another medical diagnosis besides MCI.

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IC-P-064: Changes of brain network connectivity in early Alzheimer's disease: Preliminary findings applying a data-driven approach

Alzheimer s & Dementia ◽

10.1016/j.jalz.2009.05.618 ◽

2009 ◽

Vol 5 (4S_Part_1) ◽

pp. P30-P30

Author(s):

Xiaowei Song ◽

Andrew McIntyre ◽

Ryan D'Arcy ◽

Kenneth Rockwood

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Network Connectivity ◽

Brain Network ◽

Data Driven ◽

Brain Network Connectivity ◽

Data Driven Approach ◽

Early Alzheimer’S Disease

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DATA-DRIVEN PARTICIPANT RECRUITMENT: FINDINGS FROM THE ALZHEIMER’S DISEASE NEUROIMAGING INITIATIVE 3

Journal of Prevention of Alzheimer's Disease ◽

10.14283/jpad.2020.5 ◽

2020 ◽

pp. 1-6

Author(s):

C. Barger ◽

J. Fockler ◽

W. Kwang ◽

S. Moore ◽

D. Flenniken ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Studies ◽

Newspaper Coverage ◽

Data Driven ◽

Participant Recruitment ◽

Recruitment Methods ◽

Data Driven Approach ◽

Recruitment Activities ◽

Survey Responses

Background: Effective and measurable participant recruitment methods are urgently needed for clinical studies in Alzheimer’s disease. Objectives: To develop methods for measuring recruitment tactics and evaluating effectiveness. Methods: Recruitment tactics for the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) were measured using web and phone analytics, campaign metrics and survey responses. Results: A total of 462 new participants were enrolled into ADNI3 through recruitment efforts. We collected metrics on recruitment activities including 82,003 unique visitors to the recruitment website and 3,335 calls to study phone numbers. The recruitment sources that produced the most screening and enrollment included online advertisements, local radio and newspaper coverage and emails and referrals from registries. Conclusions: Analysis of recruitment activity obtained through tracking methods provided some insight for effective recruitment. ADNI3 can serve as an example of how a data-driven approach to centralized participant recruitment can be utilized to facilitate clinical research.

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