Application of Machine Learning to Predict the Risk of Alzheimer's Disease: An Accurate and Practical Solution for Early Diagnostics

TL;DR

This paper develops a machine learning model that predicts Alzheimer's disease with high accuracy using minimal clinical tests and visits, enabling earlier and more cost-effective diagnosis.

Contribution

It introduces a practical, minimally invasive predictive model for Alzheimer's that maintains high accuracy, validated across multiple large datasets and optimized for fewer clinical visits.

Findings

Achieved over 90% accuracy and recall in predicting AD.

Demonstrated robustness of results with fewer tests and visits.

Produced a lean diagnostic protocol with 87% accuracy using only 3 tests and 4 visits.

Abstract

Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. This paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests, in hopes of earlier and cheaper diagnoses. That earlier diagnoses could be critical in the effectiveness of any drug or medical treatment to cure this disease. Our model is trained and validated using demographic, biomarker and cognitive test data from two prominent research studies: Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker Lifestyle Flagship Study of Aging (AIBL). We systematically explore different machine learning models, pre-processing methods and feature selection techniques. The most performant model demonstrates greater than 90% accuracy and recall in predicting AD, and the results generalize across sub-studies of ADNI and to the independent AIBL study. We also demonstrate that these results are robust to reducing the number of clinical visits or tests per visit. Using a metaclassification algorithm and longitudinal data analysis we are able to produce a "lean" diagnostic protocol with only 3 tests and 4 clinical visits that can predict Alzheimer's development with 87% accuracy and 79% recall. This novel work can be adapted into a practical early diagnostic tool for predicting the development of Alzheimer's that maximizes accuracy while minimizing the number of necessary diagnostic tests and clinical visits.