| 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] |