Big Data Analytics in Healthcare: A Review of Opportunities and Challenges

Abstract

Big data analytics is a rapidly expanding issue in computer engineering, and health informatics is one of the most challenging topics. Big data investigation in healthcare could certainly make improvements to medical study as well as the quality of treatment offered to patients. Machine Learning (ML) algorithms due to powerful and efficient handling of data analytics could gain knowledge from data to discover patterns and trends in the database to make predictive models. Our study aims to review the most recent scholarly publications about big data analytics and its applications which include predictive models in healthcare. A systematic search of articles in the three most significant scientific databases: ScienceDirect, PubMed, and IEEE Xplore was carried out following the PRISMA methodology. This study shows how machine learning algorithms are evolving into a promising field for supporting intelligent decisions by analyzing large data sets and thereby improving treatments while reducing costs. However, there remain challenges to overcome and there is still room for improvement to develop methods and applications. Finally, we outline the unsolved issue and the future perspectives for health sciences in the big data era.

Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms

“…This is further exacerbated by the lack of strong mapping between the current symptom-based nosology and the underlying biology (Owen, 2014). Given this multivariate complexity, studies have increasingly turned to machine-learning approaches that can utilize large amounts of data to provide individual-level predictions by the use of dimensionality reduction and pattern recognition (Cao & Schwarz, 2020;Mansourvar M., Wiil U.K., & C., 2020). The aim is to provide a better mapping of brain imaging data to symptoms, cognition and behavior.…”