Machine Learning Models for Alzheimer’s Disease Prediction: A Comparative Study

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive and functional abilities. Early detection and accurate prediction of AD progression are critical for effective intervention and management. This study explores the predictive potential of machine learning algorithms, including Linear Regression, Multilayer Perceptron, SMOreg, Random Forest, Random Tree, and REP Tree, applied to Alzheimer’s-related datasets. The dataset comprises features such as demographic (ID, gender, handedness, age, education, socioeconomic status), cognitive (MMSE, CDR), and structural (ETIV, NWBV, ASF) attributes. Comprehensive analysis reveals the strengths and limitations of each model in handling the diverse dataset characteristics. The results demonstrate that tree-based methods like Random Forest and REP Tree provide superior accuracy, while neural network-based approaches like Multilayer Perceptron effectively capture nonlinear relationships. This research underscores the importance of integrating cognitive and structural metrics to enhance predictive capabilities, offering valuable insights for early diagnosis and personalized care strategies in Alzheimer’s disease.