期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：8
页码：148-158
出版社：Science and Information Society (SAI)
摘要：Dementia is considered one of the greatest global
health and social care challenges in the 21st century. Fortunately,
dementia can be delayed or possibly prevented by changes in
lifestyle as dictated through known modifiable risk factors. These
risk factors include low education, hypertension, obesity, hearing
loss, depression, diabetes, physical inactivity, smoking, and social
isolation. Other risk factors are non-modifiable and include
aging and genetics. The main goal of this study is to demonstrate
how machine learning methods can help predict dementia based
on an individual’s modifiable risk factors profile. We use publicly
available datasets for training algorithms to predict participant’
s cognitive state diagnosis, as cognitive normal or mild cognitive
impairment or dementia. Several approaches were implemented
using data from the Alzheimer’s Disease Neuroimaging Initiative
(ADNI) longitudinal study. The best classification results were
obtained using both the Lancet and the Libra risk factor lists via
longitudinal datasets, which outperformed cross-sectional
baseline datasets. Moreover, using only data of the most recent
visits provided even better results than using the complete
longitudinal set. A binary classification (dementia vs. nondementia)
yielded approximately 92% accuracy, while the full
multi-class prediction performance yielded to a 77% accuracy
using logistic regression, followed by random forest with 92%
and 70% respectively. The results demonstrate the utility of
machine learning in the prediction of cognitive impairment based
on modifiable risk factors and may encourage interventions to
reduce the prevalence or severity of the condition in large
populations.
关键词：Machine learning; classification; data mining; data
preparation; dementia; modifiable risk factors
