Research Area:  Machine Learning

Abstract:

Today, data banks contain unpredictable data. Together with the advances in data science, large data offer the potential to better understand the causes of diseases. This potential results from the processing, analysis or modeling of machine learning algorithms. Various data sets stored in different institutions are not always shared directly due to privacy and legal concerns. This problem limits the full use of large data in health research. Federated learning is aimed at developing artificial intelligence systems based on both high accuracy and data privacy.

Keywords:  

Author(s) Name:  Ahmet Ali SÜZEN , Mehmet Ali ŞİMŞEK

Publisher name:  TRDIZION

DOI:  https://doi.org/10.37696/nkmj.660762

Volume Information:  

Paper Link:   https://app.trdizin.gov.tr/makale/TXpZeU5EY3dNQT09/a-novel-approach-to-machine-learning-application-to-protection-privacy-data-in-healthcare-federated-learning