其他摘要：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 syste matic review and the meta - analysis. The s e technique s 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 th e classification. I n this paper, we use unsupervised machine learning methodologies to describe a simple yet effective algorithm that , given a dataset with missing data, complete s such data, which lead s to a mo re 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.
