%A Mario Rojo Vicente
%X This paper focuses on the objective a Machine Learning model to classify the advancement of Parkinson´s Disease on a patient given some audio recordings and general information, such as age and gender. For this we will utilize a dataset extracted from synapse to train the several proposed models and finally extract conclusions on the outcomes. This project focuses on the implementation of CNN models based of the VGG-16 architecture. The results ranges from a 30% accuracy to around a 60% on all three datasets (train, validation and test) depending on the model and its hyper parameters, as well as the processes applied on the data. In conclusion the project was lacking more instances of reliable data but shows the possibilities of implementing such models on bigger datasets with rather significant results. The final results of this paper allow us to define rules and procedures to be implemented in similar future projects.
%I ETSI_Sistemas_Infor
%D 2022
%K Machine learning; Parkinson; Data sets
%L upm71151
%T Exploration of Parkinson’s disease recognition space by artificial neural networks
%C Madrid