  Edge computing is a fast-growing and much needed technology in healthcare.
The problem of implementing artificial intelligence on edge devices is the
complexity and high resource intensity of the most known neural network data
analysis methods and algorithms. The difficulty of implementing these methods
on low-power microcontrollers with small memory size calls for the development
of new effective algorithms for neural networks. This study presents a new
method for analyzing medical data based on the LogNNet neural network, which
uses chaotic mappings to transform input information. The method effectively
solves classification problems and calculates risk factors for the presence of
a disease in a patient according to a set of medical health indicators. The
efficiency of LogNNet in assessing perinatal risk is illustrated on
cardiotocogram data obtained from the UC Irvine machine learning repository.
The classification accuracy reaches ~91% with the ~3-10 kB of RAM used on the
Arduino microcontroller. Using the LogNNet network trained on a publicly
available database of the Israeli Ministry of Health, a service concept for
COVID-19 express testing is provided. A classification accuracy of ~95% is
achieved, and ~0.6 kB of RAM is used. In all examples, the model is tested
using standard classification quality metrics: precision, recall, and
F1-measure. The LogNNet architecture allows the implementation of artificial
intelligence on medical peripherals of the Internet of Things with low RAM
resources and can be used in clinical decision support systems.
