Background: Alzheimer's is a common neurodegenerative disease that predominantly affects the elderly. However, the leading causes for the development of Alzheimer's disease (AD) are yet to be identified, and early detection and disease progression intervention are studied to help slow down its deterioration. Recognizing the possible threat of AD in individuals before neurodegenerative changes start to take effect may contribute to the discovery of preventive actions. This study aims to identify socio-demographic, lifestyle, and neuropsychological factors that may contribute to AD development. Results: This study performed the Pearson's chi-squared test for categorical and one-way ANOVA for continuous variables to analyse the association of risk factors for developing AD from two separate cohorts. The patients in the first cohort have no record of the AD development period. In the second cohort, all the AD patients developed dementia within 36 months. Marital status, occupation, APOE4 genotype, Geriatric Depression Scale, and Functional Questionnaire Assessment score were significantly different between normal control and AD patients from both cohorts. The prediction models developed with either cohort showed high performance in ROC (receiver operating characteristic) measurements. Conclusion: This study investigated the effects of socio-demographic and neuropsychological risk factors in AD development from two different cohorts. The significant risk factors from both cohorts can contribute to developing an Alzheimer's risk app that can be potentially reliable and easily accessible.
|Title of host publication||Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021|
|Editors||Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li|
|Number of pages||8|
|Publication status||Published - 2021|