  Patients are often encouraged to make use of wearable devices for remote
collection and monitoring of health data. This adoption of wearables results in
a significant increase in the volume of data collected and transmitted. The
battery life of the devices is then quickly diminished due to the high
processing requirements of the devices. 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
for network transmission may improve sensor battery life without compromising
accuracy. There is a trade-off between efficiency and accuracy which can be
controlled by adjusting the sampling and transmission rates. This paper
demonstrates that machine learning can be used to analyse complex health data
metrics such as the accuracy and efficiency of data transmission to overcome
the trade-off problem. The study uses time series nonlinear autoregressive
neural network algorithms to enhance both data metrics by taking fewer samples
to transmit. The algorithms were tested with a standard heart rate dataset to
compare their accuracy and efficiency. The result showed that the
Levenbery-Marquardt algorithm was the best performer with an efficiency of 3.33
and accuracy of 79.17%, which is similar to other algorithms accuracy but
demonstrates improved efficiency. This proves that machine learning can improve
without sacrificing a metric over the other compared to the existing methods
with high efficiency.
