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

## TL;DR

This study develops a neural network model combined with a novel pre-processing algorithm to predict Alzheimer's disease progression month-by-month, aiding early diagnosis and clinical trial candidate identification.

## Contribution

Introduces the 'All-Pairs' pre-processing technique and demonstrates the effectiveness of neural networks in predicting AD progression from clinical data.

## Key findings

- Neural network achieved an mAUC of 0.866 in predictions.
- The model successfully predicted AD progression in both normal and MCI patients.
- The approach can identify early-stage AD patients for clinical trials.

## Abstract

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.