Using GANs and MLP Artificial Neural Networks to support early diagnosis of Alzheimer’s disease: a study on the potential of artificial data expansion

The life expectancy of the population in the most developed countries is growing every day and, consequently, there is an increase in various age-related diseases. In Brazil, just over 1.1 million people have Alzheimer’s disease (AD). In 2019, according to the World Health Organization, Alzheimer’s disease and other dementias were the third leading cause of mortality in the Americas and Europe. Despite being a degenerative and irreversible disease, if diagnosed early, treatments can be performed to slow the progression of symptoms and ensure a better quality of life for the patient. Most papers that study Computational Intelligence solutions to support diagnosis follow an approach based on neuroimaging evidence. In addition to this, another approach that has been gaining prominence is biochemical and molecular analysis. Following this approach, Ray et al., Ravetti & Moscato and Dantas & Valença carried out studies with classifiers from statistics or Computational Intelligence to support the early diagnosis of the disease. The work was carried out from a dataset with values of 120 blood proteins. Through this, they were able to classify whether or not the patient could be diagnosed with AD. As a result, Ray et al., Ravetti & Moscato and Dantas & Valença obtained an average accuracy rate of 89%, 93% and 94.34%, respectively. Thus, this work aims to use a traditional approach with a proposed Multilayer Perceptron Artificial Neural Network model to perform the early diagnosis of a patient with or without AD and compare the results obtained with the results of the related works cited. In addition, this work has as main objective to evaluate the potential of using synthetic data generated using a Generative Adversarial Network in the training and tests of the proposed classification model.