  Dementia is the fifth cause of death worldwide with 10 million new cases
every year. Healthcare applications using machine learning techniques have
almost reached the physical limits while more data is becoming available
resulting from the increasing rate of diagnosis. Recent research in Quantum
Machine Learning (QML) techniques have found different approaches that may be
useful to accelerate the training process of existing machine learning models
and provide an alternative to learn more complex patterns. This work aims to
report a real-world application of a Quantum Machine Learning Algorithm, in
particular, we found that using the implemented version for Variational Quantum
Classiffication (VQC) in IBM's framework Qiskit allows predicting dementia in
elderly patients, this approach proves to provide more consistent results when
compared with a classical Support Vector Machine (SVM) with a linear kernel
using different number of features.
