Alzheimer’s disease prediction using clinical data approach

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.