Background:\u00a0<\/strong>Research in aging, impairment, and Alzheimer’s disease (AD) often requires powerful computational models for discriminating between clinical cohorts and identifying early biomarkers and key risk or protective factors. Machine Learning (ML) approaches represent a diverse set of data-driven tools for performing such tasks in big or complex datasets. We present systematic demonstration analyses to compare seven frequently used ML classifier models and two eXplainable Artificial Intelligence (XAI) techniques on multiple performance metrics for a common neurodegenerative disease dataset. The aim is to identify and characterize the best performing ML and XAI algorithms for the present data.<\/p>\n Method:\u00a0<\/strong>We accessed a Canadian Consortium on Neurodegeneration in Aging dataset featuring four well-characterized cohorts: Cognitively Unimpaired (CU), Subjective Cognitive Impairment (SCI), Mild Cognitive Impairment (MCI), and AD (N<\/i>\u00a0= 255). All participants contributed 102 multi-modal biomarkers and risk factors. Seven ML algorithms were compared along six performance metrics in discriminating between cohorts. Two XAI algorithms were compared using five performance and five similarity metrics.<\/p>\n Results:\u00a0<\/strong>Although all ML models performed relatively well in the extreme-cohort comparison (CU\/AD), the Super Learner (SL), Random Forest (RF) and Gradient-Boosted trees (GB) algorithms excelled in the challenging near-cohort comparisons (CU\/SCI). For the XAI interpretation comparison, SHapley Additive exPlanations (SHAP) generally outperformed Local Interpretable Model agnostic Explanation (LIME) in key performance properties.<\/p>\n Conclusion:\u00a0<\/strong>The ML results indicate that two tree-based methods (RF and GB) are reliable and effective as initial models for classification tasks involving discrete clinical aging and neurodegeneration data. In the XAI phase, SHAP performed better than LIME due to lower computational time (when applied to RF and GB) and incorporation of feature interactions, leading to more reliable results.<\/p>\n In recent years, dementia researchers have begun to use complex methods for collecting and analyzing the information contributed by participants. Some of these methods use specific techniques derived from advances in \u00ab\u00a0Artificial Intelligence\u00a0\u00bb, which is often referred to as \u00ab\u00a0AI\u00a0\u00bb. A specific aspect of AI has been applied to research on dementia. That aspect is limited to helping researchers understand the patterns of information contributed by persons participating in the research. One of the AI techniques is called \u00ab\u00a0Machine Learning\u00a0\u00bb. In this study, we compared many different machine learning techniques to identify those that work best for most dementia research. We used information provided to the CCNA database for this study. We found that several machine learning techniques provide the best understanding of this information. We recommend these specific techniques to be used by other researchers in dementia.<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false},"studies-relation":[51],"class_list":["post-12725","ccna_publication","type-ccna_publication","status-publish","hentry","studies-relation-compass-nd-fr"],"acf":[],"yoast_head":"\nPlain Language Summary<\/h2>\n