# Multimodal, multiclass prediction of disease status in Alzheimer’s

> **NIH NIH F30** · UNIVERSITY OF CALIFORNIA-IRVINE · 2022 · $42,223

## Abstract

Project Summary
As the number of Americans living with Alzheimer’s disease (AD) is projected to reach 13 million by 2050, we
must prioritize efforts for early disease detection. The bottleneck of early AD detection has greatly hindered
clinical treatment and development of successful therapeutics. With greater availability of multimodal AD
biomarkers in clinical practice, we have a unique opportunity to leverage statistical machine learning for earlier
detection of AD. Past work has demonstrated good classification accuracy of clinical diagnosis of AD using
binary classifications (i.e., AD-dementia vs healthy cognition, HC; mild cognitive impairment, MCI vs HC; AD vs
MCI). These classifiers, however, are often reliant on unimodal biomarker inputs, and no formal comparison of
multimodal biomarker integration (“fusion”) methods exist for predicting either AD clinical diagnosis or
biomarker status as defined by the A/T(N) framework. Lack of optimal multimodal fusion strategies and holistic
diagnosis prediction beyond binary classification reduce the translational value of statistical machine learning
classifiers in clinical practice. This proposal fills these gaps by evaluating several competing strategies for
multimodal fusion and multiclass classification (e.g., AD vs MCI vs HC) using data from the National
Alzheimer’s Coordinating Center and Alzheimer’s Disease Neuroimaging Initiative. The strength of using large,
multimodal datasets for disease prediction is accompanied by the challenge of handling missing data, a barrier
for building a reliable classifier. This proposal will address these challenges with two specific aims: (1) compare
techniques for optimal data imputation and multimodal fusion, and (2) develop a multiclass model to accurately
predict AD status (AD/MCI/HC and A+T+/A+T-/A-T-) using multimodal inputs. Preliminary analyses of
multimodal data fusion in binary classification using random forest and sparse group lasso classifiers motivate
Aim 1. Preliminary analysis of two strategies of multiclass classification demonstrates feasibility of developing a
multimodal, multiclass classifier for Aim 2. The proposed work will be enhanced by the excellent training and
research environment at the University of California, Irvine (UCI), including direct access to 1 of 33 NIA-funded
Alzheimer’s Disease Research Centers (ADRCs). The ADRC offers a third, independent dataset to serve as a
validation set to improve the rigor of results from the proposed experiments. The applicant will be supported by
the joint mentorship of Dr. Craig Stark, the ADRC Biomarker Core Leader, and Dr. Babak Shahbaba, Director
of the UCI Data Science Initiative, and will receive advanced training in both aging and AD research and
statistics and machine learning techniques. Fellowship training will be further strengthened by the additional
mentorship of Dr. Peter Chang for machine learning, Dr. Michele Guindani for multimodal data fusion, and Dr.
S. Ahmad Sajjadi for clin...

## Key facts

- **NIH application ID:** 10538418
- **Project number:** 1F30AG079610-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Yueqi Ren
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $42,223
- **Award type:** 1
- **Project period:** 2022-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10538418

## Citation

> US National Institutes of Health, RePORTER application 10538418, Multimodal, multiclass prediction of disease status in Alzheimer’s (1F30AG079610-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10538418. Licensed CC0.

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