Repositori URV

Identificador: imarina:6388636

Handle: https://hdl.handle.net/20.500.11797/imarina6388636

Autors:
Moreno-García CAceves-Martins MSerratosa F

Resum:
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 metaanalysis, 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 metaanalysis of obesity prevention scientific papers, where 66.6% of the outcomes are missing.