Application of Pattern Recognition to Medical Data Analysis

2019 ◽  
pp. 169-195
Author(s):  
Kendall Preston
Keyword(s):  
Pattern Recognition ◽  
Data Analysis ◽  
Medical Data

Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT

2021 ◽  
pp. 175-204
Author(s):  
S. N. Kumar ◽  
A. Lenin Fred ◽  
L. R. Jonisha Miriam ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás ◽  
...  
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Medical Data

THE “KVAZAR” PACKAGE FOR PATTERN RECOGNITION AND ITS APPLICATIONS

1993 ◽  
Vol 03 (04) ◽  
pp. 439-444 ◽  
Author(s):  
VLADIMIR S. KAZANTSEV
Keyword(s):  
Experimental Data ◽  
Pattern Recognition ◽  
Data Analysis ◽  
Linear Inequalities ◽  
Processing Data ◽  
Experimental Data Analysis ◽  
Ibm Pc

The package of applied programs named KVAZAR has been elaborated to be used for classification, diagnostic, predicative, experimental data analysis problems. The package may be used in medicine, biology, geology, economics, engineering and some other problems. The algorithmical base of the package is the method of pattern recognition, based on the linear inequalities and committee constructions. Other algorithms are used too. The package KVAZAR is intended to be used with IBM PC AT/XT. The range of processing data is bounded by 40,000 numbers.

scholarly journals Computing support for advanced medical data analysis and imaging

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Wojciech Wiślicki ◽  
Tomasz Bednarski ◽  
Piotr Białas ◽  
Eryk Czerwiński ◽  
Łukasz Kapłon ◽  
...  
Keyword(s):  
Data Analysis ◽  
Medical Data ◽  
Computing Support

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

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6209
Author(s):  
Andrei Velichko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Support ◽  
Data Analysis ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Classification Accuracy ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Data

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.

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