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Edge computing is a fast-growing and much needed technology in healthcare.The problem of implementing artificial intelligence on edge devices is thecomplexity and high resource intensity of the most known neural network dataanalysis methods and algorithms. The difficulty of implementing these methodson low-power microcontrollers with small memory size calls for the developmentof new effective algorithms for neural networks. This study presents a newmethod for analyzing medical data based on the LogNNet neural network, whichuses chaotic mappings to transform input information. The method effectivelysolves classification problems and calculates risk factors for the presence ofa disease in a patient according to a set of medical health indicators. Theefficiency of LogNNet in assessing perinatal risk is illustrated oncardiotocogram data obtained from the UC Irvine machine learning repository.The classification accuracy reaches ~91% with the ~3-10 kB of RAM used on theArduino microcontroller. Using the LogNNet network trained on a publiclyavailable database of the Israeli Ministry of Health, a service concept forCOVID-19 express testing is provided. A classification accuracy of ~95% isachieved, and ~0.6 kB of RAM is used. In all examples, the model is testedusing standard classification quality metrics: precision, recall, andF1-measure. The LogNNet architecture allows the implementation of artificialintelligence on medical peripherals of the Internet of Things with low RAMresources and can be used in clinical decision support systems.

A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare