Abstract:
Patients with Mild Cognitive Impairment (MCI) face an increased risk of developing
Alzheimer's disease (AD), highlighting the importance of early diagnosis for effective
interventions and management of the disease. In our study, we investigated the
progression of AD in patients initially diagnosed with MCI using multimodal
longitudinal data. A classification based framework was proposed for MCI prediction
with baseline data of 569 stable MCI (sMCI) and 268 progressive MCI (pMCI) patients.
Employing three supervised machine learning (ML) algorithms—support vector machine
(SVM), logistic regression (LR), Random Forest (RF) and incorporating features such as
cognitive function assessments, MRI, PET scans, CSF biomarkers, and genetic APOE
status, the classification accuracies of 83.4%, 80.2%, and 80% were achieved
respectively. Significant differences were observed in the performance of the models,
with the SVM notably outperforming both LR and RF (p < 0.05). Impaired memory
function and lower clinical tests scores were found as primary indicators of MCI patients
progressing towards AD. Although the fusion of all modalities yielded accurate results
for predicting MCI progression to AD, our analysis revealed less significant differences
in evaluation metrics when only cognitive test results were used as features. This suggests
that cognitive assessments alone are nearly as effective in predicting MCI progression,
which can lead to more cost-effective strategies in clinical settings. This study
underscores the need for further research aimed at developing new tools to assist
clinicians in prognostic decision making.
