Background Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a
substantial economic burden. The Random forest algorithm is effective in predicting AD; …

Background: Currently, there is no objective, clinically available tool for the accurate
diagnosis of Alzheimer's disease (AD). There is a pressing need for a novel, minimally …

Introduction: This study aimed to develop novel machine learning models for predicting
Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We …

Dementia is a well‐known syndrome and Alzheimer's disease (AD) is the main cause of
dementia. Lipids play a key role in the pathogenesis of AD, however, the prediction value of …

Objectives This study helps in predicting the incidence of AD based on multiple variables
that were identified during the prodromal phase. Identification and timely intervention delays …

Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or
may not progress into the AD, is the most ordinary form of dementia. It is extremely important …

Alzheimer's disease (AD) is the leading cause of dementia in older adults. There is currently
a lot of interest in applying machine learning to find out metabolic diseases like Alzheimer's …

Alzheimer disease (AD) is a heterogeneous and complex disease in which different
pathophysiological mechanisms are involved. This heterogenicity can be reflected in …

Diagnosing Alzheimer's disease (AD) is usually difficult, especially when the disease is in its
early stage. However, treatment is most likely to be effective at this stage; improving the …

Objective Metabolic biomarkers can potentially inform disease progression in Alzheimer's
disease (AD). The purpose of this study is to identify and describe a new set of diagnostic …