The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Privacy Preserving Feature Selection for Vertically Distributed Medical Data Based on Genetic Algorithms and Naïve Bayes
Abstract
Machine learning is a powerful tool to mine useful knowledge from vast databases. Many establishments in the medical area such as hospitals, laboratories want to join their efforts with the ambition to extract models that are more accurate. However, this approach faces problems. Due to the laws protecting patient privacy or other similar concerns, parties are reluctant to share their data. In vast amounts of data, which are useful and pertinent in constructing accurate data mining models? In this article, the researchers deal with these challenges for vertically distributed medical data. They propose an original secure wrapper solution to perform feature selection based on genetic algorithms and distributed Naïve Bayes. Contrary to the previous solutions, the original data is not perturbed. Therefore, the data utility and performance are preserved. They prove that the proposed solution selects relevant attributes to increase performance, preserving patient privacy.
Related Content
|
Shailendra Aote, Mukesh M. Raghuwanshi.
© 2021.
34 pages.
|
|
Anjana Mishra, Bighnaraj Naik, Suresh Kumar Srichandan.
© 2021.
15 pages.
|
|
Thendral Puyalnithi, Madhuviswanatham Vankadara.
© 2021.
15 pages.
|
|
Geng Zhang, Xiansheng Gong, Xirui Chen.
© 2021.
13 pages.
|
|
Jhuma Ray, Siddhartha Bhattacharyya, N. Bhupendro Singh.
© 2021.
19 pages.
|
|
Pijush Samui, Viswanathan R., Jagan J., Pradeep U. Kurup.
© 2021.
18 pages.
|
|
Ravinesh C. Deo, Sujan Ghimire, Nathan J. Downs, Nawin Raj.
© 2021.
32 pages.
|
|
|