Biomarkers Selection Toward Early Detection of Alzheimer's Disease

• Published in: 2020 IEEE International Conference on Electro Information Technology (EIT) pp. 487 - 494
• Main Authors: Gorji, Hamed Taheri, Khoei, Tala Talaei, Kaabouch, Naima
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2020
• Subjects: Adaboost; Alzheimer's Disease; biomarker; Biomarkers; Correlation; Dementia; Feature extraction; feature selection; Magnetic resonance imaging; Mild Cognitive Impairment; Positron emission tomography
• ISSN: 2154-0373
• DOI: 10.1109/EIT48999.2020.9208258

Alzheimer's disease (AD) is a neurodegenerative brain disorder and the fifth leading cause of death among people aged 65 and older. Based on recent research, it was found that in addition to cognitive tests, quantitative biomarkers can be useful indicators for monitoring the progress from Mild Cognitive Impairment (MCI) to Alzheimer's disease. Hence, identifying the most relevant biomarkers and cognitive tests can lead to a more reliable and accurate diagnosis of AD. Therefore, this study aims to identify the most pertinent cognitive tests and biomarkers, features, to detect Alzheimer's disease. This aim is achieved by using six conventional feature selection methods. In addition, we used a feature combination approach to find the best subset of the features that can lead to the highest accuracy in differentiating between healthy subjects, early mild cognitive impairment (EMCI), and AD patients. Unlike conventional feature selection methods that select the Clinical Dementia Rating Scale Sum of Boxes (CDRSB) as a unique feature, the proposed feature combination method selects this CDRSB as well as the Middle temporal gyrus (MidTemp). The results show that this combination gives the highest accuracy in differentiating between cognitively normal (CN), EMCI, and AD groups.