# Forecasting the Progression of Alzheimer's Disease Using Neural Networks and a Novel Pre-Processing Algorithm

arXiv: https://arxiv.org/abs/1903.07510 · HTML: https://mlsanity.com/paper/1903.07510/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 7 of 7)

- [Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review](https://mlsanity.com/paper/2108.04174/) (2021)
- [Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data](https://mlsanity.com/paper/2210.13889/) (2022)
- [Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA)](https://mlsanity.com/paper/2211.00302/) (2022)
- [Interpretable Weighted Siamese Network to Predict the Time to Onset of Alzheimer's Disease from MRI Images](https://mlsanity.com/paper/2304.07097/) (2023)
- [Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease](https://mlsanity.com/paper/2401.14130/) (2024)
- [Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI](https://mlsanity.com/paper/2501.15733/) (2025)
- [Quantum Model Parallelism for MRI-Based Classification of Alzheimer's Disease Stages](https://mlsanity.com/paper/2602.00128/) (2026)

## Abstract (arXiv)

Alzheimer's disease (AD) is the most common neurodegenerative disease in older people. Despite considerable efforts to find a cure for AD, there is a 99.6% failure rate of clinical trials for AD drugs, likely because AD patients cannot easily be identified at early stages. This project investigated machine learning approaches to predict the clinical state of patients in future years to benefit AD research. Clinical data from 1737 patients was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and was processed using the "All-Pairs" technique, a novel methodology created for this project involving the comparison of all possible pairs of temporal data points for each patient. This data was then used to train various machine learning models. Models were evaluated using 7-fold cross-validation on the training dataset and confirmed using data from a separate testing dataset (110 patients). A neural network model was effective (mAUC = 0.866) at predicting the progression of AD on a month-by-month basis, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.