Table 10.
Applications of big data analytics in healthcare domain.
| S. no | Citations | Architectures | Description |
|---|---|---|---|
| 1 | 193 | Proper treatment | Healthcare big data analytics has enhanced the capabilities of nursing staff by practicing data science and analysis for accurate treatment using patients’ clinical-relevant historical information |
| 2 | 59 | Lupus disease diagnosing | This research work developed lupus disease diagnosing system with the help of big data analytics |
| 3 | 64 | Silico medicine solutions | Integrating VPH technologies with healthcare big data analytics a robust and effective solution is developed in this research work that provides a silico medicine solution for individuals |
| 4 | 200 | Real-time health analysis | A smart real-time eHealth application is presented in this paper by embedding Amazon and Microsoft Azure services to solve health issues in a limited time |
| 6 | 71 | Alzheimer’s disease detection | A statistical data analysis mechanism is proposed in this research work for the identification of Alzheimer’s disease using Chronic nervous system and healthcare big data analytics |
| 7 | 88 | health risk assessment | Multiple eHealth applications are overviewed in this research work to develop an optimal technique for patient’s health stratification and risk assessment purposes |
| 9 | 93 | eHearing loss application | An embedded solution is proposed in this research work for hearing loss (HL) patients. This application uses holistic management scheme to the HL-patients by facilitating public health services. This integrated solution also keeps the users away from this type of diseases in future |
| 11 | 110,201 | High storage capabilities | Big data has a large amount of data to be processed, particularly a plethora of types of data to process and extract enriched information regarding a problem of interest. Furthermore, storing this vast amount of data is another big challenge for the research community. Apache Pig and Apache HBase are the repository facilitates the research community to store this huge amount of information easily and provides an easy access interface for the retrieval purposes |
| 12 | 114 | Health status detection | This research work presents a machine learning-based model for checking the patient’s health status. Apache Spark-based cloud computing and data processing engine is deployed to process tweets (related to a specific patient’s health status) |
| 13 | 121 | Hadoop framework | Using healthcare big data analytics, this study work proposes a statistical diagnosing mechanism for diabetic analysis. Hadoop framework is used for the validation and implementation purposes |
| 14 | 128,131,142 | Indoor treatment system | In this over-populous and disease burden environment accessing hospitals and doctors for basic health purposes is more difficult. But this problem become more severe for old age patients. To address this problem, these research articles propose an IoT-based smart eHelath application that facilitates aged personnel at doorstep |
| 15 | 7 | Intelligent hospital appointment system | A smart hospital appointment system was developed in this research article by using the concept of first come-first serve (FIFO) concept. This model not only vanished the concept of traditional doctor-patient appointment system but also helps in fixing the appointment with an expert doctor using healthcare data bank |
| 16 | 139 | Digital HIV/AIDS detection system | This research article presents an electronic HIV/AIDS detection system using healthcare big data analytics. Then a regional based monitoring system is developed to monitor these patients. This model not only helps in identifying the HIV/AIDS infected patient in a specific region, but it also helps the practitioners, researchers, and government agencies to perform proper treatment of these patients |
| 17 | 145 | Parkinson disease diagnosing model | This study has developed a sensor-based robot model for Parkinson disease diagnosing purposes. Sensors are integrated in the infected patient’s body. Based on the sensed data the robot moves with the patient and accumulate data. This accumulated data was then provided to artificial neural network for classification and recognition of Parkinson disease |