Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> II. METHODOLOGY <s> Advances in high-throughput and mass-storage technologies have led to an information explosion in both biology and medicine, presenting novel challenges for analysis and modeling. With regards to multivariate analysis techniques such as clustering, classification, and regression, large datasets present unique and often misunderstood challenges. The authors' goal is to provide a discussion of the salient problems encountered in the analysis of large datasets as they relate to modeling and inference to inform a principled and generalizable analysis and highlight the interdisciplinary nature of these challenges. The authors present a detailed study of germane issues including high dimensionality, multiple testing, scientific significance, dependence, information measurement, and information management with a focus on appropriate methodologies available to address these concerns. A firm understanding of the challenges and statistical technology involved ultimately contributes to better science. The authors further suggest that the community consider facilitating discussion through interdisciplinary panels, invited papers and curriculum enhancement to establish guidelines for analysis and reporting. <s> BIB001 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> II. METHODOLOGY <s> During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and representative training-set compounds to learn robust decision rules. The explosive growth in the amount of public domain-available chemical and biological data has generated huge effort to design, analyze, and apply novel learning methodologies. Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS). In addition, I analyze several relevant VS studies from recent publications, providing a detailed view of the current state-of-the-art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances. <s> BIB002
The healthcare system is facing the digital transformations with an enhancement in the use of healthcare information system, electronic medical record, wearable, handheld, and smart devices. A huge amount of data is produced from these digital transformations. The current increase and development in healthcare big data and its computation in the field of cardiology enable researchers and practitioners to extract and visualise the new insights from medical big data to further provide easy solution for it. The huge amount of data in healthcare is to be assessed to identify, discover, and prevent different diseases. The analysis of big data is quite difficult, as big data contains huge sum of information and record(s). The analysis of big data can be exploited by different algorithms of data mining to mine useful informations and patterns for further investigation of disease and their prevention BIB002 . The heart disease is a big issue in health and most of the time becomes the cause of death. The identification of heart disease on early time is important in the medical healthcare and research field  . The analytics big data has improved the healthcare by facilitating personalised medicine based on analytical prescription. Data sharing model of three layer for big data of heart is developed which uncover the scope of cardiac remodelling  . The big data in healthcare can be categorized in three forms, for example small n and large p, large n and small p, and large n and large p, (n is the number of samples while p is the number of parameters) BIB001 . There are different categories under which big data features of cardiology can be placed. For example in cardiology, shortness of breath, typical chest pain, feet edema, palpitation, syncopy, dyspnea on exertion, unconscious (Rhythm disturbance), hypertension, fever, skin rash/joint pain, murmurs, and so on. Several studies are available to identify different techniques to define data features. A data feature uniquely identifies data and hence used to detect a specific disease.