  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.
