Raúl Fernández Ruiz is a researcher in the field of biomedical data analytics, with a specific focus on machine learning methods for medical diagnosis and privacy preservation. He has published several papers that demonstrate his expertise in this area.

Fernández Ruiz has developed non-invasive deep learning analysis techniques for classifying Smith-Magenis Syndrome (SMS), a rare genetic disorder that affects children. His research aims to improve diagnostic accuracy and reduce the need for invasive tests.

He has also explored the use of machine learning methods for identifying SMS cases, with an experimental evaluation demonstrating the effectiveness of these approaches. Additionally, Fernández Ruiz has investigated the voice quality of children with SMS, highlighting potential biomarkers for early detection.

Furthermore, he has worked on privacy-preserving methodologies in biomedical data analytics. In particular, his research on Accelerated Garble Embedding Methodology (AGE) aims to ensure the confidentiality and integrity of sensitive health data while still allowing for meaningful insights to be gained from large datasets.

Overall, Fernández Ruiz's work is characterized by its focus on developing innovative machine learning solutions that can improve healthcare outcomes, particularly in the context of rare genetic disorders.

Here are Raúl Fernández Ruiz's publications: 

-ANALYSIS OF VOICE QUALITY IN CHILDREN WITH SMITH-MAGENIS SYNDROME

-COMPARISON OF AN ACCELERATED GARBLE EMBEDDING METHODOLOGY FOR PRIVACY PRESERVING IN BIOMEDICAL DATA ANALYTICS

-IDENTIFICATION OF SMITH–MAGENIS SYNDROME CASES THROUGH AN EXPERIMENTAL EVALUATION OF MACHINE LEARNING METHODS

-NONINVASIVE DEEP LEARNING ANALYSIS FOR SMITH–MAGENIS SYNDROME CLASSIFICATION

-RESEARCH GROUP IN BIOINSPIRED SYSTEMS AND APPLICATIONS OF THE REY JUAN CARLOS UNIVERSITY.