Machine learning in biomedical and health big data: a comprehensive survey with empirical and experimental insights - Kamal Taha (Journal of Big Data)
Abstract This article delves into the application of machine learning within the realm of biomedical and health big data. We present both empirical and experimental assessments of diverse machine learning methodologies, providing a comprehensive examination of current techniques in big data analytics. Our discussion includes analyses and evaluations that underscore the utility and limitations of various ML methods, aimed at empowering researchers and practitioners in their decision-making processes. Additionally, this article highlights prospective advancements in ML techniques that could further elevate big data applications in healthcare, illuminating future research directions in the field. By bridging empirical evaluations with theoretical insights, this survey aims to furnish a well-rounded perspective on machine learning implementations. This in-depth analysis offers valuable guidance for enhancing the development and deployment of future ML systems in biomedical and health data contexts.