A deep learning model for Alzheimer's disease diagnosis based on patient clinical records.

ABSTRACT

BACKGROUND: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning is a promising tool to minimize the impact of under-diagnosis. OBJECTIVE: To develop a Deep Learning model that uses clinical data from patients with dementia to classify whether they have AD. METHODS: A Deep Learning model to identify AD in clinical records is proposed. In addition, several rebalancing methods have been used to preprocess the dataset and several studies have been carried out to tune up the model. RESULTS: Model has been tested against other well-established machine learning techniques, having better results than these in terms of AUC with alpha less than 0.05. CONCLUSIONS: The developed Neural Network Model has a good performance and can be an accurate assisting tool for AD diagnosis.