Deep Learning-Based Dementia Prediction Using Multimodal Data

David Ortiz-Perez, Pablo Ruiz-Ponce, David Tomás, José García Rodríguez

Abstract: In this paper we propose a deep architecture to predict dementia, a disease which affects around 55 million people all over the world and makes them in some cases dependent people. To this end, we have used the DementiaBank dataset, which includes audio recordings as well as their transcriptions of healthy people and people with dementia. Different models have been used and tested, including Convolutional Neural Networks for the audio classification, Transformers for the text classification and a combination of both models in a multimodal one. These models have been tested over a test set, obtaining the best results from the text modality, achieving a 90.36% of accuracy on the detection of dementia task.