An overview of machine learning in health related areas: pitfalls and opportunities

Abstract

Machine learning techniques are on the spotlight in current scientific literature and these methods are gaining prominence in the health field. However, there are a few considerations that must be taken before conducting a study with machine learning techniques. This paper aims to provide an overview of machine learning methods applied to studies of health related areas. Additionally, this article will discuss important points about data preparation that may influence on the prediction outcome; comparison with statistical analysis; and potential applications. A literature search was carried out, using IEEE xplore and Pubmed, of publications from the last 10 years. Undoubtedly machine learning is becoming more and more present in science. However, the unfamiliarity with this technology may hinder or jeopardize its application. As any scientific tool, machine learning presents positive points along with limitations and both aspects should be considered in every analysis. The researcher must select the most adequate method and consider all repercussions of data preparation on the predictive model. A special attention should be given towards distance based techniques. ML techniques are full with potential applications; however these methods did not replace classical statistical analysis and, yet, they will continue to be an important tool to in health areas.