Abstract
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 non- dementia) 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.