Predictive analysis using machine learning: Review of trends and methods

Artificial Intelligence (AI) has been growing considerably over the last ten years. Machine Learning (ML) is probably the most popular branch of AI to date. Most systems that use ML methods use them to perform predictive analysis. This paper aims to conduct a literature review of trends and methods of machine learning used for predictive analysis. To do this, we carried out a collection of research papers from three scientific databases. We then considered selection criteria in order to study only papers published in the last five years, prioritizing those published in peer-reviewed scientific journals. This process led to the selection of 30 research papers that were considered for this review. The purpose of this study is to provide researchers, companies or anyone wishing to perform predictive analysis with clues that will enable them to choose the best ML method(s) according to its field of application, based on the latest research works in the literature. This study highlighted the most used methods by field of application: DT and ANN in education, LR, RF and DT in building, DT in botany, RF and ANN in social science and RF in medicine.