# Machine Learning Approach for Predicting Amyloid and Tau Positivity in Alzheimer's Disease Using Clinically Accessible Features

## TL;DR

This study uses machine learning to accurately predict Alzheimer's disease biomarkers using easily collected clinical data, which could help in screening for the disease.

## Contribution

A novel machine learning model is developed to predict amyloid and tau positivity using clinically accessible features with high accuracy.

## Key findings

- The model achieved mean AUCs of 0.89 and 0.83 for predicting amyloid and tau positivity in model building and validation cohorts.
- Cognitive impairment was the most impactful feature for predicting biomarker positivity.
- Higher age, lower MoCA scores, and female sex increased the probability of biomarker positivity.

## Abstract

Prediction of Alzheimer's disease (AD) biomarkers can improve public health strategies, especially if achieved with easily collectable data in a single consultation. Machine learning (ML) offers versatile tools for clinical and research applications. This study investigated a ML model's ability to predict amyloid and tau positivity using easily obtainable features.

Individuals with amyloid and tau status were selected from ADNI, TRIAD, and PPMI datasets (Model Building) and from the NACC dataset (Validation). Shared clinical features included age, sex, education, clinical diagnosis, MoCA scores, and BMI. Amyloid positivity was defined by amyloid‐PET (PIB‐PET, FBB‐PET, or AZD4694‐PET) or CSF AB42, and tau positivity by Tau‐PET (MK6240‐PET, AV1451‐PET) or CSF p‐tau181. For NACC, positivity derived from fields AMYLPET and TAUPETAD. Data processing is summarized in Figure 1.

The Model Building sample included 1593 individuals (mean MoCA 25.3 ± 4.3). The Validation cohort included 861 individuals (mean MoCA 22.2 ± 2.8). The model achieved high performance for predicting amyloid and tau positivity, with mean AUCs of 0.89 and 0.83 for Model Building and Validation, respectively (Figure 2a, 2b). In Validation, high sensitivity (0.93) came at the expense of specificity (0.65), while Model Building showed balanced sensitivity and specificity (0.81 and 0.83). Higher age, lower MoCA, cognitive impairment, female sex, and lower BMI increased the probability of positivity (Figure 2c, 2d). Cognitive impairment was the most impactful feature in both Model Building and Validation datasets, followed by MoCA and age (Figure 2e, 2f).

Predicting AD biomarkers using ML and readily collectable features is feasible and accurate. The model's high sensitivity indicates a potential research utility in clinical trials for population screening to minimize false negatives. Future efforts should enhance generalizability, explore additional features, and prioritize real‐world validation.