Supervised Learning Model

Applications in 5G

Model Adoption in healthcare delivery

References

1

ML and statistical logistic regression

Self-organized LTE dense small cell deployments with dynamic frequency and bandwidth allocation

This model gives healthcare providers the chance to specifically target at-risk individuals who need a more individualized health plan to help them improve their everyday health habits.

[24] [35] [36]

2

Support Vector Machines (SVM)

Model for predicting path loss in metropolitan areas

This model is a potential classification strategy for the healthcare industry, and in smart hospitals, it is generally used to forecast drug adherence.

[37]

3

Neural-Network-based approximation

Channel learning to infer unobservable CSI from an observable channel

This model is generally applied for forecasting e.g. It can be applied to forecast kidney disease.

[24]

4

Supervised ML Frameworks

TDD uplink-downlink configuration in XG-PON-LTE systems is adjusted to optimize network performance based on current traffic conditions in the hybrid optical-wireless network.

This model can be used as a predictive analytics tool to identify and cure illnesses before they pose a serious threat to human life.

[35] [36]

5

Artificial Neural Networks (ANN), and Multi-Layer Perceptrons (MLPs)

For next-generation wireless networks, modeling, and approximations of objective functions for link budget and propagation loss are needed.

The cost of creating medicines is decreased by using these models to identify a safe and effective medication option from a set of databases.

[35] [37]