Integrating Brain Imaging for Outcome Prediction in Alzheimer’s Disease
The most common form of dementia, Alzheimer’s is a progressive disease that causes memory loss and interferes with cognitive function. Currently, no cure exists, but extensive research is being conducted in hopes of identifying possible treatments. In the research field, one particular area of focus is early detection of the disease, from which brain imaging has emerged as a powerful diagnostic tool. Using machine learning, we wanted to see how well models that were trained on brain imaging data could diagnose patients.
Data collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to train the machine learning models. We employed various models, including support vector machines, ensemble classifiers, and deep neural networks. Using Jupyter Notebook, we developed a pipeline for preprocessing the raw data, fitting the models to the training data, and evaluating the models on the test data. Overall, the machine learning models appeared to distinguish between healthy controls and patients with Alzheimer’s, with a prediction accuracy around 80%. Diagnostic performance for the intermediate stages of the disease was less conclusive.
Comments