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

Abstract

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.

arXiv comment20 pages, 7 figures, 9 tables

References in corpus (12)

• Recent Advances in Physical Reservoir Computing: A Review
• Pruning Convolutional Neural Networks for Resource Efficient Inference
• To prune, or not to prune: exploring the efficacy of pruning for model compression
• Adaptive Extreme Edge Computing for Wearable Devices
• Deep Rewiring: Training very sparse deep networks
• FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
• Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map
• A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks
• Learning Sparse Low-Precision Neural Networks With Learnable Regularization
• Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
• Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network
• An improved LogNNet classifier for IoT application

Cited by in corpus (2)

• Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network
• A Novel Approach for Estimating Largest Lyapunov Exponents in One-Dimensional Chaotic Time Series Using Machine Learning