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

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

The clinical and radiological outcomes of hip resurfacing versus total hip arthroplasty: a meta-analysis and systematic review.

On the basis of the current evidence base, HRS may have better functional outcomes than THA, but the increased risks of heterotopic ossification, aseptic loosening, and revision surgery following HRS indicate that THA is superior in terms of implant survival