Skip to main content
. 2022 Dec 26;12:22377. doi: 10.1038/s41598-022-26090-5

Table 7.

List of key features used for data integration purposes.

S. no Citation Key features Description
1 42 Structural features Using the CT scan images and healthcare big data analytics, this paper identified the normal and abnormal brain structure condition with the help of structural based features
2 193 Behaviour-based features This article summarizes the action-based (exemplar-based) features to improve the capabilities of nursing staff in some unwanted and emergency situations. It mainly focuses on the exploration of exemplars from nursing informatics research, to develop a smart mechanism that is feasible for nursing staff to participate in the big data revolution
3 60,119 Semantic based features Around the world, a standard format of documents relevant to healthcare are suggested for the healthcare assessment and analysis process but the companies and the researchers face a big barrier during its comprehension and timely processing. To address this problem Hadoop based semantic transformation model is presented in this research work by using semantic based features with the help of CAD architecture
4 64 Heterogeneous data features For monitoring quality of life of an individual patient several features are considered for the analysis and assessment processes. These features include non-textual information, heterogeneous data spaces, organ and organism scale, and specified analytics for identifying “physiological envelope” for a patient routine assessment purpose
5 67,83 Activity-based features Social networking is an alternate source for retrieving information regarding daily healthcare activities. Social media facilitates the user by proving an easy access environment without any long time wait in queue or interruption compared to the traditional health pooling stations, where both younger and elder people wait for long time for their turn to poll. A Facebook application is proposed in this research work, where more than 1400 employees selected to accumulate patient routine-based activity information. This data can be collected in relatively small interval of time compared to the traditional arguing system without any promotional action
6 105 5 V features 5 V features are outlined in this book chapter to integrate both structural and non-structural data in healthcare big data domain. These features include volume, velocity, veracity, value, and variety-based features. It also consists other features such as: vulnerability and complexity
7 44 Real-Time analysis-based features This research article presents a real time scenario for feature accumulation and disease diagnosing purposes. In this case study seven patient were contributed for the prediction of seizure disease using support vector machine
8 36 Integrated features With rapid improvement and ever-increasing data in healthcare domain make it more complex to retrieve data regarding specific research oriented and disease diagnosing problem. To address this critical problem Tian36 presented the concept of integrated features to integrate CT scan images in big data domain for efficient retrieval and disease diagnosing purposes
9 194 Functional features This systematic review work has outlined the use of cognitive computing and functional features reported in healthcare, cybersecurity, and big data