Unsupervised Machine Learning Application to Perform a Systematic Review and Meta-Analysis in Medical Research

Authors

  • Carlos Francisco Moreno-García Universitat Rovira i Virgili
  • Magaly Aceves-Martins Universitat Rovira i Virgili
  • Francesc Serratosa Universitat Rovira i Virgili

DOI:

https://doi.org/10.13053/cys-20-1-2360

Keywords:

Systematic review, meta-analysis, unsupervised machine learning, recommender systems, principal component analysis.

Abstract

When trying to synthesize information from multiple sources and perform a statistical review to compare them, particularly in the medical research field, several statistical tools are available, most common are the systematic review and the meta-analysis. These techniques allow the comparison of the effectiveness or success among a group of studies. However, a problem of these tools is that if the information to be compared is incomplete or mismatched between two or more studies, the comparison becomes an arduous task. On a parallel line, machine learning methodologies have been proven to be a reliable resource, such software is developed to classify several variables and learn from previous experiences to improve the classification. In this paper, we use unsupervised machine learning methodologies to describe a simple yet effective algorithm that, given a dataset with missing data, completes such data, which leads to a more complete systematic review and meta-analysis, capable of presenting a final effectiveness or success rating between studies. Our method is first validated in a movie ranking database scenario, and then used in a real life systematic review and meta-analysis of obesity prevention scientific papers, where 66.6% of the outcomes are missing.

Author Biographies

Carlos Francisco Moreno-García, Universitat Rovira i Virgili

was born in Mexico City in 1988. He received his Master degree in Computer Science from Universitat Rovira i Virgili (Tarragona, Spain) in 2012. He is currently a Ph. D. student at the same institution, where he is a member of the Sensorial Systems Applied to the Industry (SSAI) research group. His areas of interest are graphs, computer vision, pattern recognition, and machine learning, and his work includes developing applications of those areas in biometrics, information security, and biomedicine. 

Magaly Aceves-Martins, Universitat Rovira i Virgili

from Mexico City, received her Master Degree in Nutrition from Universitat Rovira i Virgili and Universitat de Barcelona (Barcelona, Spain) in 2012. She is currently a Ph. D. student at Universitat Rovira i Virgili, where she is a member of the Nutrition Functional, Oxidation and Cardiovascular disease (N-FOC SALUT) research group. Her main line of research is health promotion in children and adolescents across Europe. 

Francesc Serratosa, Universitat Rovira i Virgili

was born in Barcelona in 1967. He received his Ph.D. from Universitat Politecnica de Catalunya (Barcelona, Spain) in 2000. He is currently a full time professor of computer science at Universitat Rovira i Virgili. Since 1993, he has been active in research in the areas of computer vision, robotics, structural pattern recognition, machine learning, and biometrics. He has published more than 100 papers and is the principal researcher of the Sensorial Systems Applied to the Industry (SSAI) research group. 

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Published

2016-03-31

Issue

Vol. 20 No. 1 (2016): Thematic Issue: Topic Trends in Computing Research (Guest Editor: Claudia P. Ayala)

Section

Articles

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