Survey highlights machine learning and deep learning methods for improving predictive accuracy in Alzheimer's disease, indicating challenges in data and integration.
Key Points
Ensemble classifiers like Random Forest and SVM achieve 93% accuracy in predicting Alzheimer's disease.
Feature engineering from MRI and CSF biomarkers significantly contributes to prediction accuracy.
Integration of multi-modal data can enhance predictive capabilities for Alzheimer's disease.
Challenges such as data heterogeneity need to be addressed for effective Alzheimer's disease prediction.