INTELLIGENT DECISION SUPPORT SYSTEM FOR RADIONUCLIDE DIAGNOSTICS IN CARDIOLOGY

The algorithm of information-extreme machine learning of decision support system for myocardial perfusion scintigraphy with multilevel system of nested control tolerances for diagnostic feature values is designed. It is proposed the use of quantitative features which characterize the brightness of t...

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Main Author: А. С. Москаленко
Format: Article
Language:English
Published: National Aerospace University «Kharkiv Aviation Institute» 2019-09-01
Series:Радіоелектронні і комп'ютерні системи
Subjects:
Online Access:http://nti.khai.edu/ojs/index.php/reks/article/view/913
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author А. С. Москаленко
author_facet А. С. Москаленко
author_sort А. С. Москаленко
collection DOAJ
description The algorithm of information-extreme machine learning of decision support system for myocardial perfusion scintigraphy with multilevel system of nested control tolerances for diagnostic feature values is designed. It is proposed the use of quantitative features which characterize the brightness of the pixels of the polar map radiopharmaceuticals distribution, and contextual features that describe the presence of symptoms, addictions and chronic diseases. The computing aspect of the normalized modification of information criterion of machine learning, which is a functional of the accuracy characteristics of decision rules, is considered. The results of machine learning with swarm optimization of control tolerances at different numbers of their levels are analyzed. Obtained an unmistakable rules on training matrix decision rules
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publisher National Aerospace University «Kharkiv Aviation Institute»
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spelling doaj.art-3a3b91f317b2414c9bbb571cca4e88922023-09-15T18:55:16ZengNational Aerospace University «Kharkiv Aviation Institute»Радіоелектронні і комп'ютерні системи1814-42252663-20122019-09-0103495510.32620/reks.2016.3.06956INTELLIGENT DECISION SUPPORT SYSTEM FOR RADIONUCLIDE DIAGNOSTICS IN CARDIOLOGYА. С. МоскаленкоThe algorithm of information-extreme machine learning of decision support system for myocardial perfusion scintigraphy with multilevel system of nested control tolerances for diagnostic feature values is designed. It is proposed the use of quantitative features which characterize the brightness of the pixels of the polar map radiopharmaceuticals distribution, and contextual features that describe the presence of symptoms, addictions and chronic diseases. The computing aspect of the normalized modification of information criterion of machine learning, which is a functional of the accuracy characteristics of decision rules, is considered. The results of machine learning with swarm optimization of control tolerances at different numbers of their levels are analyzed. Obtained an unmistakable rules on training matrix decision ruleshttp://nti.khai.edu/ojs/index.php/reks/article/view/913перфузія міокарда, інформаційний критерій, система контрольних допусків, машинне навчання, категоріальні ознаки, розпізнавання образів
spellingShingle А. С. Москаленко
INTELLIGENT DECISION SUPPORT SYSTEM FOR RADIONUCLIDE DIAGNOSTICS IN CARDIOLOGY
Радіоелектронні і комп'ютерні системи
перфузія міокарда, інформаційний критерій, система контрольних допусків, машинне навчання, категоріальні ознаки, розпізнавання образів
title INTELLIGENT DECISION SUPPORT SYSTEM FOR RADIONUCLIDE DIAGNOSTICS IN CARDIOLOGY
title_full INTELLIGENT DECISION SUPPORT SYSTEM FOR RADIONUCLIDE DIAGNOSTICS IN CARDIOLOGY
title_fullStr INTELLIGENT DECISION SUPPORT SYSTEM FOR RADIONUCLIDE DIAGNOSTICS IN CARDIOLOGY
title_full_unstemmed INTELLIGENT DECISION SUPPORT SYSTEM FOR RADIONUCLIDE DIAGNOSTICS IN CARDIOLOGY
title_short INTELLIGENT DECISION SUPPORT SYSTEM FOR RADIONUCLIDE DIAGNOSTICS IN CARDIOLOGY
title_sort intelligent decision support system for radionuclide diagnostics in cardiology
topic перфузія міокарда, інформаційний критерій, система контрольних допусків, машинне навчання, категоріальні ознаки, розпізнавання образів
url http://nti.khai.edu/ojs/index.php/reks/article/view/913
work_keys_str_mv AT asmoskalenko intelligentdecisionsupportsystemforradionuclidediagnosticsincardiology