Key result
An optimizable tree machine learning approach yielded the best accuracy and minimum classification error for evaluating spectrum sensing in cognitive radio-based smart healthcare systems.
Why the study?
Cognitive radio can exploit primary user spectrum to transmit patient data in smart healthcare, but tree-based machine learning algorithms needed investigation for evaluating spectrum sensing.
Optimizable tree machine learning algorithms provide the highest accuracy for spectrum sensing in cognitive radio-based smart healthcare systems.
May support cognitive radio for sensor data transmission in smart healthcare; hypothesis-generating and requires prospective validation.
The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients' health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient's health data by exploiting the primary user's (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).
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Raza et al. (2023) studied this question. Optimizable tree machine learning approach vs. Other tree-based algorithms (fine, coarse, ensemble boosted, medium, ensemble bagged, ensemble RUSBoosted) was evaluated on Training and testing accuracies and minimum classification error (MCE). An optimizable tree machine learning approach yielded the best accuracy and minimum classification error for evaluating spectrum sensing in cognitive radio-based smart healthcare systems.
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