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Having a cognitive and self-optimizing network that proactively adapts notonly to channel conditions, but also according to its users needs can be one ofthe highest forthcoming priorities of future 6G Heterogeneous Networks(HetNets). In this paper, we introduce an interdisciplinary approach linkingthe concepts of e-healthcare, priority, big data analytics (BDA) and radioresource optimization in a multi-tier 5G network. We employ three machinelearning (ML) algorithms, namely, naive Bayesian (NB) classifier, logisticregression (LR), and decision tree (DT), working as an ensemble system toanalyze historical medical records of stroke out-patients (OPs) and readingsfrom body-attached internet-of-things (IoT) sensors to predict the likelihoodof an imminent stroke. We convert the stroke likelihood into a risk factorfunctioning as a priority in a mixed integer linear programming (MILP)optimization model. Hence, the task is to optimally allocate physical resourceblocks (PRBs) to HetNet users while prioritizing OPs by granting them high gainPRBs according to the severity of their medical state. Thus, empowering the OPsto send their critical data to their healthcare provider with minimized delay.To that end, two optimization approaches are proposed, a weighted sum ratemaximization (WSRMax) approach and a proportional fairness (PF) approach. Theproposed approaches increased the OPs average signal to interference plus noise(SINR) by 57% and 95%, respectively. The WSRMax approach increased the systemtotal SINR to a level higher than that of the PF approach, nevertheless, the PFapproach yielded higher SINRs for the OPs, better fairness and a lower marginof error.

Patient-centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks