Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/31947Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Mishra, Ayush Chandra | - |
| dc.contributor.author | Chaubey, Ratnesh | - |
| dc.contributor.author | Ojha, Smriti | - |
| dc.contributor.author | Mishra, Sudhanshu | - |
| dc.contributor.author | Sekar, Mahendran | - |
| dc.contributor.author | Verma, Swati | - |
| dc.contributor.author | (UniKL RCMP) | - |
| dc.date.accessioned | 2025-05-07T03:52:26Z | - |
| dc.date.available | 2025-05-07T03:52:26Z | - |
| dc.date.issued | 2023-08 | - |
| dc.identifier.citation | Big data in Oncology: impact, challenges, and risk assessment. (2023, December 30). https://www.riverpublishers.com/book_details.php?book_id=1062 | en_US |
| dc.identifier.isbn | 9788770228121 | - |
| dc.identifier.issn | 9788770228138 | - |
| dc.identifier.uri | https://www.riverpublishers.com/book_details.php?book_id=1062 | - |
| dc.identifier.uri | https://ir.unikl.edu.my/jspui/handle/123456789/31947 | - |
| dc.description.abstract | In this modern era, big data provides an enormous amount of collected data about various diseases in India and throughout the world, which is created by data available in vast diversity, high speed, and volume. Because such data is becoming more prevalent, methods for handling and extracting insights from it must be studied and provided. Furthermore, decision-makers should be able to retrieve important from such a distinct, diverse, and frequently changing dataset, which encompasses everything from ordinary transactions to customer/patient interactions, as well as social media sites and other digital platforms. Big data is viewed as a revolution with the potential to change the way businesses function in a variety of industries. This study investigates the research data available from existing challenges and cases and applies big data analytics to enhance cancer risk categorization. We have divided the input data into three categories: pathology, radiomics, and population health management. Big data provides computational methodologies which will improve clinical risk stratification for cancer patients in a minimum period. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | River Publishers | en_US |
| dc.subject | Big Data | en_US |
| dc.subject | Cancer | en_US |
| dc.subject | Clinical Rill Stratification | en_US |
| dc.subject | Computational Approach | en_US |
| dc.subject | Datasets | en_US |
| dc.subject | Disease | en_US |
| dc.subject | Predictive Analysis | en_US |
| dc.title | Various cancer analysis using big data analysis technology-An advanced approach | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | Journal Articles | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Various Cancer Analysis Using Big Data Analysis Technology An Advanced Approach.pdf | 252.59 kB | Adobe PDF | View/Open |
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