Privacy-preserving Hybrid Learning Framework for Healthcare
Résumé
In recent years, there has been a significant increase in the volume of data and the number of datasets in the healthcare industry, and this trend is expected to continue and intensify. Various strategies are being developed to analyze the data. Nevertheless, these strategies are extensively segregated according to the specific data formats and disorders. Privacy-preserving Hybrid Learning
Framework for Healthcare. The framework introduces a hybrid learning technique to achieve efficient decision-making. We propose integrating a data meshing approach to tackle the challenge of interoperability and heterogeneity with multiple data sources. Furthermore, this paper identifies the potential privacy challenges for machine learning-based healthcare applications that operate with multiple data sources and demand the excessive computation of cloud computing. In addition, we present a comprehensive use case for forecasting cardiovascular disease. The detailed use case and scenarios highlight how our proposal can improve the decision-making process.