Big data analytics covers integration of heterogeneous data, data quality control, analysis, modeling, interpretation, and validation. (Halevi, 2014) Application of big data analytics provides comprehensive knowledge discovering from the available huge amount of data. Big data analytics seeks to leverage improvements in computer science to address these needs.
Analytical approaches can be divided in three categories, namely descriptive, predictive, and prescriptive. (Magnuson J.A., 2020) Overall, Big Data Analytics can be understood as an umbrella term for data analysis applications in the context of Big Data, namely using algorithms to analyze data: regression analysis, simulation, supervised and unsupervised machine learning methods, among others. (Halevi, 2014; Watson, 2019)Such applications of big data analytics can improve the patient-based service, to detect earlier spreading of diseases, generate new insights into disease mechanisms, monitor the quality of the medical and healthcare institutions as well as provide better treatment methods or increase cos-effectiveness in health interventions. (Watson, 2019) Such applications are , however, not free from limitations or disadvantages. (Pastorino et al., 2019)