SINUS NODE DYSFUNCTION SYNDROME: MAKING DECISION BY QUANTUM GENETIC ALGORITHM, GRAPH NEURAL NETWORKS

Quantum genetic algorithms, graph neural networks using for making decisions by sinus node dysfunction syndrome diagnosis are present in this publication.


Introduction
It's known that genetic algorithms, graph neural networks are basic directions [1] to artificial intellect formation. Deep Neural Networks for ECG analysis was close to the standard in clinical practice [2]. The purpose of this study was to determine principles of quantum genetic algorithm, graph neural networks using for using for making decisions by sinus node dysfunction syndrome.
Methods Subjective, objective and additional investigations were as diagnostic criteria for sinus node dysfunction syndrome diagnosis [3,4]. Etiology of sinus node dysfunction syndrome presented on visual programming language "Dragon" [5] ( fig. 1).
Additional investigation for diagnosis of sinus node dysfunction syndrome as predisposing to formulation of clinical diagnosis presented on visual programming language "Dragon" [5] (fig. 3).
We were used Typical Conceptual Spaces by some principles [6] as information is organized by quality dimensions that are sorted into domains; domains are endowed with a topology or metric; similarity is represented by distance in a conceptual space [6].
We used our modified domains [7] 2. Preliminary diagnosis of sinus node dysfunction syndrome by visual programming on language "Dragon" [5].
Example of the quantum genetic algorithm using for differential diagnosis of antonym, oxymoron like heart electrical instabilities for sinus node dysfunction syndrome or binodal syndrome by some qubit chromosomes [7]

Results
Implementation of our algorithms on visual programming language "Dragon", using modified domains of Typical Conceptual Spaces, quantum genetic algorithms gave us possibilities to graph neural networks decision-making of sinus node dysfunction syndrome diagnosis (Fig. 4): We used Shannon [8,10,11] and Renyi entropy [9] for sinus node dysfunction syndrome diagnosis. Fig. 4. Graph neural network using for making decisions by sinus node dysfunction syndrome diagnosis

Discussion
Principles of conceptual spaces domains, Shannon and Renyi entropy, quantum genetic algorithms, graph neural networks using for making decisions for sinus node dysfunction diagnosis presented in this article.
Some authors [12] developed an algorithm, which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. On dataset, they [12] train a 34-layer convolutional neural network, which maps a sequence of ECG samples to a sequence of rhythm classes. They [12] exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value). Key to exceeding expert performance is a deep convolutional network, which can map a sequence of ECG samples to a sequence of arrhythmia annotations along with a novel dataset two orders of magnitude larger than previous datasets of its kind [12].
Different investigations [13,14,15,16] gave data that antisymmetry is the symmetry of elements that are identical in shape, but contrasting in "content".
Sinus node dysfunction of different etiology is the symmetry of elements that are identical in shape, but contrasting in "content" by etiological peculiarities. It's necessary to build additional neuronets by others criteria of subjective, objective and additional investigations, according to etiology, pathogenesis of this syndrome. Conclusion 1. Quantum genetic algorithm, graph neural networks using for making decisions for sinus node dysfunction diagnosis presented in this investigation. СЕКЦІЯ XXI. МЕДИЧНІ НАУКИ ТА ГРОМАДСЬКЕ ЗДОРОВ'Я 2. Shannon and Renyi entropy of heart electrical instabilities were as criteria for sinus node dysfunction syndrome diagnosis, for graph neural networks building.
3. It's necessary to build additional neuronets by others criteria of subjective, objective and additional investigations, according to etiology, pathogenesis of this syndrome.