Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals

Abstract

Background: Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations.

Methods: Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance.

Results: The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors.

Conclusion: Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.

Keywords: Alzheimer’s disease; machine learning; mild cognitive impairment; older people; predictors.

Figure 1

DCA curve for the best fusion model (AdaBoost with logistic regression as the base estimator) on test set. The horizontal coordinate is the threshold probability and the ordinate is the net benefit. As can be seen from the figure, the final model has positive benefits in the whole threshold interval, which means that it has certain clinical value.

Figure 2

Violin box diagram of prediction score. Score represents the predicted probability of the final model (AdaBoost with logistic regression as the base estimator) for each individual, statistically significant differences of prediction scores were observed between the MCI and ad groups in the validation (left) and test (right) sets (independent samples T-test). It can be seen that the final model has the ability to distinguish between MCI and ad.

Figure 3

Ranking of feature importance at different time intervals. The same colours represent the same features. It can be seen that the importance of features changes in different time intervals, generally, FAQ, ADAS11 and mPACCtrailsB ranked the top 3.