Using Machine Learning and Big Data Analytics to Prioritize Outpatients in HetNets

TL;DR

This paper presents machine learning and optimization techniques to prioritize outpatients in HetNets based on health status, ensuring reliable, low-delay connectivity for critical medical data transmission.

Contribution

It introduces a novel system combining naive Bayesian classification with MILP-based resource allocation to dynamically prioritize outpatients in heterogeneous networks.

Findings

Effective prioritization of outpatients based on health risk.

Improved connectivity reliability for critical medical data.

System adapts to changes in patients' health conditions.

Abstract

In this paper, we introduce machine learning approaches that are used to prioritize outpatients (OP) according to their current health state, resulting in self-optimizing heterogeneous networks (HetNet) that intelligently adapt according to users' needs. We use a na\"ive Bayesian classifier to analyze data acquired from OPs' medical records, alongside data from medical Internet of Things (IoT) sensors that provide the current state of the OP. We use this machine learning algorithm to calculate the likelihood of a life-threatening medical condition, in this case an imminent stroke. An OP is assigned high-powered resource blocks (RBs) according to the seriousness of their current health state, enabling them to remain connected and send their critical data to the designated medical facility with minimal delay. Using a mixed integer linear programming formulation (MILP), we present two approaches to optimizing the uplink side of a HetNet in terms of user-RB assignment: a Weighted Sum Rate Maximization (WSRMax) approach and a Proportional Fairness (PF) approach. Using these approaches, we illustrate the utility of the proposed system in terms of providing reliable connectivity to medical IoT sensors, enabling the OPs to maintain the quality and speed of their connection. Moreover, we demonstrate how system response can change according to alterations in the OPs' medical conditions.