Predicting Amyloid-β Positivity in Alzheimer’s Disease: Comprehensive Analysis of Feature Selection and Machine Learning Models for Accurate Identification

Abstract

To accurately identify individuals at risk of Alzheimer’s disease (AD), it is crucial to develop precise tools for predicting amyloid-β (Aβ) positivity in the brain. We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to analyze 1,377 human subjects. These participants were divided into five groups: cognitive normal (CN), subjective memory complaints (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and confirmed AD. Each group was further divided into ten subgroups based on sex, resulting in a comprehensive analysis. The dataset was used to create and evaluate the performance of 15 machine learning (ML) models. A set of 17 potential predictors was generated by combining variables from different categories, including six demographic factors (such as age), ten measurements (such as ADAS13), and APOE4 status. Through ML-based predictive modeling, several cognitive assessment measures, including ADAS13, demonstrate significant importance in multiple ML models. The highest accuracies in the 10 subgroups were 0.875, 0.892, 0.778, 0.850, 0.771, 0.739, 0.781, 0.791, 0.879, and 0.903, respectively. The collection of ML models consists of practical and valuable risk feature scores that can significantly enhance the identification of individuals who are likely to test positive for Aβ.