Machine Learning for Early Prediction of Cognitive Decline in Alzheimer's Disease

Early identification of cognitive decline is a crucial challenge in Alzheimer’s research, with significant implications for therapeutic intervention and disease management. Currently, available treatments merely decelerate the progression of the disease, without achieving a complete halt, consequently researchers are concentrating their efforts on a key aspect that concerns the early prediction and prevention of Alzheimer’s disease (AD) in order to delay the onset and progression. However, initial clinical manifestations are not always decisive, and diagnosis often occurs at an advanced stage of cognitive impairment. Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, this study proposes an interpretable machine learning (ML) model for predicting mild cognitive impairment (MCI) from cognitively normal (CN) patients based on multimodal baseline biomarkers. After a thorough preprocessing phase and a features selection step, an optimized Random Forest (RF) classifier was implemented. The results obtained show an overall accuracy of the model of 76%, with a sensitivity of 64% and specificity of 84%, confirming the key role of cognitive, neurostructural and metabolic biomarkers in predicting the risk of progression to MCI. This study demonstrates the potential of ML in early prediction of cognitive decline, laying the foundation for more effective and personalized diagnostic tools in the context of neurodegenerative diseases.