Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach
International psychogeriatrics, 2020•cambridge.org
Objectives: To use a Machine Learning (ML) approach to compare Neuropsychiatric
Symptoms (NPS) in participants of a longitudinal study who developed dementia and those
who did not. Design: Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated
using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1
score, and Cohen's kappa) were computed for each algorithm, and graphic metrics (ROC
and precision-recall curves) and features analysis were computed for the best-performing …
Symptoms (NPS) in participants of a longitudinal study who developed dementia and those
who did not. Design: Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated
using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1
score, and Cohen's kappa) were computed for each algorithm, and graphic metrics (ROC
and precision-recall curves) and features analysis were computed for the best-performing …
Objectives
To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not.
Design
Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1 score, and Cohen’s kappa) were computed for each algorithm, and graphic metrics (ROC and precision-recall curves) and features analysis were computed for the best-performing algorithm.
Setting
Primary care health centers.
Participants
128 participants: 78 cognitively unimpaired and 50 with MCI.
Measurements
Diagnosis at baseline, months from the baseline assessment until the 3rd follow-up or development of dementia, gender, age, Charlson Comorbidity Index, Neuropsychiatric Inventory-Questionnaire (NPI-Q) individual items, NPI-Q total severity, and total stress score and Geriatric Depression Scale-15 items (GDS-15) total score.
Results
30 participants developed dementia, while 98 did not. Most of the participants who developed dementia were diagnosed at baseline with amnestic multidomain MCI. The Random Forest Plot model provided the metrics that best predicted conversion to dementia (e.g. accuracy=.88, F1=.67, and Cohen’s kappa=.63). The algorithm indicated the importance of the metrics, in the following (decreasing) order: months from first assessment, age, the diagnostic group at baseline, total NPI-Q severity score, total NPI-Q stress score, and GDS-15 total score.
Conclusions
ML is a valuable technique for detecting the risk of conversion to dementia in MCI patients. Some NPS proxies, including NPI-Q total severity score, NPI-Q total stress score, and GDS-15 total score, were deemed as the most important variables for predicting conversion, adding further support to the hypothesis that some NPS are associated with a higher risk of dementia in MCI.
Cambridge University Press
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