Throughput Prediction in Smart Healthcare Network using Machine Learning Approaches

Abstract

Modern-day healthcare is being challenged by the critical nature of traffic requirements for services like health monitoring, remote consultations, and robotic surgery. While the existing literature addresses various algorithms for real-world throughput prediction, there is a lack of systematic exploration of these algorithms in healthcare. In this paper, we study the problem of accurately predicting performance parameters in a smart healthcare environment. Taking into account the characteristics of healthcare networks, we compare machine learning based algorithms and prediction models for performance and performance characteristics. We designed a model based on the Healthcare 4.0 paradigm for throughput prediction of two different types of healthcare data - sensor data used for telemonitoring e.g., Blood Pressure (BP) and Sp02 sensor data, and audio/video data that is used for teleconsultation. Among the algorithms, K-means performs the best achieving 72.68% accuracy and 75.9% sensitivity, while Naive Bayes gives better precision at 72% in predicting the throughput.