  Having a cognitive and self-optimizing network that proactively adapts not
only to channel conditions, but also according to its users needs can be one of
the highest forthcoming priorities of future 6G Heterogeneous Networks
(HetNets). In this paper, we introduce an interdisciplinary approach linking
the concepts of e-healthcare, priority, big data analytics (BDA) and radio
resource optimization in a multi-tier 5G network. We employ three machine
learning (ML) algorithms, namely, naive Bayesian (NB) classifier, logistic
regression (LR), and decision tree (DT), working as an ensemble system to
analyze historical medical records of stroke out-patients (OPs) and readings
from body-attached internet-of-things (IoT) sensors to predict the likelihood
of an imminent stroke. We convert the stroke likelihood into a risk factor
functioning as a priority in a mixed integer linear programming (MILP)
optimization model. Hence, the task is to optimally allocate physical resource
blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain
PRBs according to the severity of their medical state. Thus, empowering the OPs
to send their critical data to their healthcare provider with minimized delay.
To that end, two optimization approaches are proposed, a weighted sum rate
maximization (WSRMax) approach and a proportional fairness (PF) approach. The
proposed approaches increased the OPs average signal to interference plus noise
(SINR) by 57% and 95%, respectively. The WSRMax approach increased the system
total SINR to a level higher than that of the PF approach, nevertheless, the PF
approach yielded higher SINRs for the OPs, better fairness and a lower margin
of error.
