Open AccessTemporal association of neuropsychological test performance using unsupervised learning reveals a distinct signature of Alzheimer's disease status
Author(s) -
Joshi Prajakta S.,
Heydari Megan,
Kannan Shruti,
Alvin Ang Ting Fang,
Qin Qiuyuan,
Liu Xue,
Mez Jesse,
Devine Sherral,
Au Rhoda,
Kolachalama Vijaya B.
Publication year - 2019
Publication title -
alzheimer's and dementia: translational research and clinical interventions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.49
H-Index - 30
ISSN - 2352-8737
DOI - 10.1016/j.trci.2019.11.006
Subject(s) - context (archaeology) , alzheimer's disease , dementia , neuropsychology , psychology , cognition , recall , disease , verbal learning , california verbal learning test , medicine , clinical psychology , developmental psychology , cognitive psychology , neuroscience , biology , paleontology
Subtle cognitive alterations that precede clinical evidence of cognitive impairment may help predict the progression to Alzheimer's disease (AD). Neuropsychological (NP) testing is an attractive modality for screening early evidence of AD. Methods Longitudinal NP and demographic data from the Framingham Heart Study (FHS; N = 1696) and the National Alzheimer's Coordinating Center (NACC; N = 689) were analyzed using an unsupervised machine learning framework. Features, including age, logical memory‐immediate and delayed recall, visual reproduction‐immediate and delayed recall, the Boston naming tests, and Trails B, were identified using feature selection, and processed further to predict the risk of development of AD. Results Our model yielded 83.07 ± 3.52% accuracy in FHS and 87.57 ± 1.19% accuracy in NACC, 80.52 ± 3.93%, 86.74 ± 1.63% sensitivity in FHS and NACC respectively, and 85.63 ± 4.71%, 88.41 ± 1.38% specificity in FHS and NACC, respectively. Discussion Our results suggest that a subset of NP tests, when analyzed using unsupervised machine learning, may help distinguish between high‐ and low‐risk individuals in the context of subsequent development of AD within 5 years. This approach could be a viable option for early AD screening in clinical practice and clinical trials.