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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach.

Ahmad Raza¹, Mohsin Ali¹, Muhammad Khurram Ehsan²

¹Department of Computer Engineering, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, Pakistan.

Sensors (Basel, Switzerland)

|September 9, 2023

Summary

This summary is machine-generated.

Optimizable tree algorithms enhance spectrum sensing in cognitive radio (CR) smart healthcare systems. This machine learning approach improves real-time patient data transmission for faster medical decisions.

Keywords:

cognitive radio machine learning optimizable tree smart healthcare spectrum sensing

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Area of Science:

Wireless Communication
Machine Learning
Healthcare Technology

Background:

Modern technology drives demand for real-time healthcare monitoring.
Smart healthcare systems utilize sensors for patient data transmission.
Cognitive radio (CR) offers spectrum-efficient data transfer for healthcare.

Purpose of the Study:

To investigate tree-based algorithms (TBAs) for spectrum sensing in CR-based smart healthcare.
To evaluate the performance of various TBAs using simulated and theoretical datasets.
To identify the most accurate TBA for spectrum sensing in this context.

Main Methods:

Generated datasets based on probability of detection (Pd) and probability of false alarm (Pf).
Trained and tested multiple TBAs: fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree.
Calculated training and testing accuracies for each TBA with varying signal samples.

Main Results:

Optimizable tree demonstrated superior accuracy in spectrum sensing.
Optimizable tree achieved the minimum classification error (MCE).
Accuracies were compared across different TBAs and dataset types.

Conclusions:

Optimizable tree is highly effective for spectrum sensing in CR-based smart healthcare.
Accurate spectrum sensing is crucial for reliable real-time healthcare data transmission.
Machine learning, specifically TBAs, offers a promising solution for enhancing smart healthcare systems.