An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network

Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network.However,due to the complexity of the human body,there are still many challenges to face in that process.One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients,online.This paper presents a novel chronic disease prediction system based on an incremental deep neural network.The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner.With time,the system can predict diabetes more and more accurately by processing the feedback information.Many diabetes prediction studies are based on a common dataset,the Pima Indians diabetes dataset,which has only eight input attributes.In order to determine the correlation between the pathological characteristics of diabetic patients and their daily living resources,we have established an in-depth cooperation with a hospital.A Chinese diabetes dataset with 575 diabetics was created.Users’data collected by different sensors were used to train the network model.We evaluated our system using a real-world diabetes dataset to confirm its effectiveness.The experimental results show that the proposed system can not only continuously monitor the users,but also give early warning of physiological data that may indicate future diabetic ailments.

A Dataflow-Oriented Programming Interface for Named Data Networking

Inheriting from a data-driven communication pattern other than a location-driven pattern, named data net- working （NDN） offers better support to network-layer dataflow. However, the application developers have to handle complex tasks, such as data segmentation, packet verification, and flow control, due to the lack of proper transport-layer protocols over the network layer. In this study, we design a dataflow-oriented programming interface to provide transport strategies for NDN, which greatly improves the efficiency in developing applications. This interface presents two application data unit; （ADU） retrieval strategies according to different data publishing patterns, in which it adopts an adaptive ADU pipelining algorithm to control the dataflow based on the current network status and data generation rate. The interface also offers network measurement strategies to monitor an abundance of critical metrics infuencing the application performance. We verify the functionality and performance of our interface by implementing a video streaming application spanning 11 time zones over the worldwide NDN testbed. Our experiments show that the interface can efficiently support developing high-performance and dataflow-driven NDN applications.