Determining the severity of Parkinson's disease in patients using a
  multi task neural network
María Teresa García-Ordás José Alberto Benítez-Andrades Jose Aveleira-Mata José-Manuel Alija-Pérez Carmen Benavides
Parkinson's disease is easy to diagnose when it is advanced, but it is very
difficult to diagnose in its early stages. Early diagnosis is essential to be
able to treat the symptoms. It impacts on daily activities and reduces the
quality of life of both the patients and their families and it is also the
second most prevalent neurodegenerative disorder after Alzheimer in people over
the age of 60. Most current studies on the prediction of Parkinson's severity
are carried out in advanced stages of the disease. In this work, the study
analyzes a set of variables that can be easily extracted from voice analysis,
making it a very non-intrusive technique. In this paper, a method based on
different deep learning techniques is proposed with two purposes. On the one
hand, to find out if a person has severe or non-severe Parkinson's disease, and
on the other hand, to determine by means of regression techniques the degree of
evolution of the disease in a given patient. The UPDRS (Unified Parkinson's
Disease Rating Scale) has been used by taking into account both the motor and
total labels, and the best results have been obtained using a mixed multi-layer
perceptron (MLP) that classifies and regresses at the same time and the most
important features of the data obtained are taken as input, using an
autoencoder. A success rate of 99.15% has been achieved in the problem of
predicting whether a person suffers from severe Parkinson's disease or
non-severe Parkinson's disease. In the degree of disease involvement prediction
problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a
full deep learning pipeline for data preprocessing and classification has
proven to be very promising in the field Parkinson's outperforming the
state-of-the-art proposals.