Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis.

Introduction: Alzheimer's disease (AD) is a progressive brain disorder that impairs cognitive functions, behavior, and memory. Early detection is crucial as it can slow down the progression of AD. However, early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests. Methods: This study introduces a data acquisition technique and a preprocessing pipeline, combined with multivariate long short-term memory (M-LSTM) and AdaBoost models. These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients, using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database. Results: The methodology proposed in this study significantly improved performance metrics. The testing accuracy reached 80% with the AdaBoost model, while the M-LSTM model achieved an accuracy of 82%. This represents a 20% increase in accuracy compared to a recent similar study. Discussion: The findings indicate that the multivariate model, specifically the M-LSTM, is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.