Background: Dementia is a complex neurological disorder, to which little is known about its mechanisms and no therapeutic treatment was identified, to date, to revert or alleviate its symptoms. It affects the older adults population causing a progressive cognitive decline that can become severe enough to impair the individuals' independence and functioning. In this scenario, the prognosis research, directed to identify modifiable risk factors in order to delay or prevent its development, in a big enough time frame is substantially important.
Objective: This study investigates a decision tree multifactorial approach for the prognosis of dementia of individuals, not diagnosed with this disorder at baseline, and their development (or not) of dementia in a time frame of 10 years.
Methods: This study retrieved data from the Swedish National Study on Aging and Care, which consisted of 726 subjects (313 males and 413 females), of which 91 presented a diagnosis of dementia at the 10-year study mark. A K-nearest neighbors multiple imputation method was employed to handle the missing data. A wrapper feature selection was employed to select the best features in a set of 75 variables, which considered factors related to demographic, social, lifestyle, medical history, biochemical test, physical examination, psychological assessment and diverse health instruments relevant to dementia evaluation. Lastly, a cost-sensitive decision tree approach was employed in order to build predictive models in an stratified nested cross-validation experimental setup.
Results: The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Our findings showed that most of the variables selected by the tree are related to modifiable risk factors, of which physical strength was an important factor across all ages of the sample. Also, there was a lack of variables related to the health instruments routinely used for the dementia diagnosis that might not be sensitive enough to predict dementia in a 10 years’ time.
Conclusions: The proposed model identified diverse modifiable factors, in a 10 years’ time from diagnosis, that could be investigated for possible interventions in order to delay or prevent the dementia onset.