Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/441432
Title: | Secured health recommendation system with deep learning approach towards analysis of medical big data |
Researcher: | Mahesh Selvi T |
Guide(s): | Kavitha V |
Keywords: | Clinical Pre Clinical and Health Clinical Medicine Medical Informatics clinical information clinical data Healthcare Applications |
University: | Anna University |
Completed Date: | 2022 |
Abstract: | Recommender Systems (RS) is an important filtering tool for discovering services in a personalized way to guide users from the large space of possible options. Therefore, it has become an essential and indispensable component of the modern industry through the Internet. In general, there are different types of filtering techniques provide services to users, such as Collaborative Filtering (CF) based recommendations, Content Based (CB) recommendations, context aware recommendations and so on. Due to this fact, RS helps users to determine suitable useful information to solve the overload problem through filtering information. Besides, RS is widely used in medical scenarios and health informatics. newlineOver the past decades, recommendation system in healthcare application has become popular due to the exponential increase in health data available on various platforms. This information can be used for patient-oriented decision-making in big data analytic systems. Currently, a massive amount of clinical data is scattered on the internet users to determine suitable information on different sites. Healthcare recommendation will be part of an integrated patient-based e-health platform that provides healthcare for the older people in need of care, including newline |
Pagination: | xviii,167p. |
URI: | http://hdl.handle.net/10603/441432 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 27.64 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 2.09 MB | Adobe PDF | View/Open | |
03_content.pdf | 88.66 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 75.15 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 1.44 MB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 361.31 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 490.26 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 380.65 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1.22 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 110.32 kB | Adobe PDF | View/Open | |
11_annexures.pdf | 176.25 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 84.45 kB | Adobe PDF | View/Open |
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