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In the healthcare system, patients are required to use wearable devices forthe remote data collection and real-time monitoring of health data and thestatus of health conditions. This adoption of wearables results in asignificant increase in the volume of data that is collected and transmitted.As the devices are run by small battery power, they can be quickly diminisheddue to the high processing requirements of the device for data collection andtransmission. Given the importance attached to medical data, it is imperativethat all transmitted data adhere to strict integrity and availabilityrequirements. Reducing the volume of healthcare data and the frequency oftransmission will improve the device battery life via using inferencealgorithm. There is an issue of improving transmission metrics with accuracyand efficiency, which trade-off each other such as increasing accuracy reducesthe efficiency. This paper demonstrates that machine learning can be used toanalyze complex health data metrics such as the accuracy and efficiency of datatransmission to overcome the trade-off problem using the Levenberg-Marquardtalgorithm to enhance both metrics by taking fewer samples to transmit whilstmaintaining the accuracy. The algorithm is tested with a standard heart ratedataset to compare the metrics. The result shows that the LMA has bestperformed with an efficiency of 3.33 times for reduced sample data size andaccuracy of 79.17%, which has the similar accuracies in 7 different samplingcases adopted for testing but demonstrates improved efficiency. These proposedmethods significantly improved both metrics using machine learning withoutsacrificing a metric over the other compared to the existing methods with highefficiency.

Enhancement of Healthcare Data Transmission using the Levenberg-Marquardt Algorithm