Enhancement of Healthcare Data Transmission using the Levenberg-Marquardt Algorithm

TL;DR

This paper presents a machine learning approach using the Levenberg-Marquardt algorithm to optimize healthcare data transmission, reducing data volume and improving efficiency while maintaining accuracy in wearable health monitoring devices.

Contribution

It introduces a novel application of the Levenberg-Marquardt algorithm to balance accuracy and efficiency in healthcare data transmission, reducing sample size without sacrificing data integrity.

Findings

Achieved 3.33 times efficiency improvement with reduced data samples.

Maintained accuracy of 79.17% across multiple sampling cases.

Outperformed existing methods in transmission efficiency without accuracy loss.

Abstract

In the healthcare system, patients are required to use wearable devices for the remote data collection and real-time monitoring of health data and the status of health conditions. This adoption of wearables results in a significant increase in the volume of data that is collected and transmitted. As the devices are run by small battery power, they can be quickly diminished due to the high processing requirements of the device for data collection and transmission. Given the importance attached to medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission will improve the device battery life via using inference algorithm. There is an issue of improving transmission metrics with accuracy and efficiency, which trade-off each other such as increasing accuracy reduces the efficiency. This paper demonstrates that machine learning can be used to analyze complex health data metrics such as the accuracy and efficiency of data transmission to overcome the trade-off problem using the Levenberg-Marquardt algorithm to enhance both metrics by taking fewer samples to transmit whilst maintaining the accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The result shows that the LMA has best performed with an efficiency of 3.33 times for reduced sample data size and accuracy of 79.17%, which has the similar accuracies in 7 different sampling cases adopted for testing but demonstrates improved efficiency. These proposed methods significantly improved both metrics using machine learning without sacrificing a metric over the other compared to the existing methods with high efficiency.