Automatic selection model to identify neurodegenerative diseases.

Objective: This study evaluates machine learning algorithms' effectiveness in classifying Parkinson's disease and Huntington's disease based on biomarker data obtained non-invasively from patients and healthy controls.

Methods: Datasets containing biomarker data (, , and values of accelerometers) from sensors were collected from Parkinson's disease, Huntington's disease patients, and healthy controls. An automatic selection model method was implemented for disease classification, using a unique Mexican database of human gait biomarkers, which we consider the only one of its kind.

The potential and challenges of Health 4.0 to face COVID-19 pandemic: a rapid review.

The COVID-19 pandemic has generated the need to evolve health services to reduce the risk of contagion and promote a collaborative environment even remotely. Advances in Industry 4.0, including the internet of things, mobile networks, cloud computing, and artificial intelligence make Health 4.

Gait Biomarkers Classification by Combining Assembled Algorithms and Deep Learning: Results of a Local Study.

Machine learning, one of the core disciplines of artificial intelligence, is an approach whose main emphasis is analytical model building. In other words, machine learning enables an automaton to make its own decisions based on a previous training process. Machine learning has revolutionized every research sector, including health care, by providing precise and accurate decisions involving minimal human interventions through pattern recognition.