Authors - Noel Milliones, Vicente Pitogo, Mark Phil Pacot Abstract - The sensitive information in the healthcare industry along with the increasing phe nomenon of the use of intelligent health-related devices makes it a very difficult task to ensure the privacy of patients as well as carry out precise analysis. The centralized methodology in cur-rent machine learning models requires the exchange of raw information of patients from different healthcare institutions and health related devices to the centralized computer system through the network. However, due to the privacy issues and network traffic issues in this methodology, the proposal proposes the development of a privacy-preserving health analytics platform. Here in this proposed methodology, every healthcare center as well as health-related device has its own local machine learning model without transferring even a single piece of information outside. However, the models also employ disease-specific models including CNN heart diseases models of 95 percent accuracy, Gradient Boosting Classifier Diabetes models of 93 percent accuracy models, along with SVM models of liver diseases along with 96 percent GridSearch models. Each edge device carries out the data preprocessing for the local environment, as well as the processes of model training and the transmission of secure updates, in such a way that the sensitive patient data has never left the environment. The platform presented proves the idea that edge computing and collaborative learning can lead to scalable and secure healthcare analytics with high predictive performance.
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