Abstract:
Alzheimer's Disease (AD) is a progressive
neurodegenerative condition that profoundly affects cognition and
memory. Due to the absence of curative treatments, early detection
and prediction are crucial for effective intervention. This study
employs machine learning and clinical data from Alzheimer's
Disease Neuroimaging Initiative (ADNI) to predict AD onset. Data
preprocessing ensures quality through variable selection and
feature extraction. Diverse machine learning algorithms,
including Naive Bayes, logistic regression, SVM-Linear, random
forest, Gradient Boosting, and Decision Trees, are evaluated for
prediction accuracy. The model resulted with random forest
classifier together with filter method yields the highest AUC. The
study highlights important analysis using Random Forest and
Decision Trees, revealing significant variables including cognitive
tests, clinical scales, demographics, brain-related metrics, and key
biomarkers. By enhancing predictive capabilities, this research
contributes to advancing Alzheimer's disease diagnosis and
intervention strategies.
