scholarly journals Coronary artery decision algorithm trained by two-step machine learning algorithm

RSC Advances ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 4014-4022
Author(s):  
Young Woo Kim ◽  
Hee-Jin Yu ◽  
Jung-Sun Kim ◽  
Jinyong Ha ◽  
Jongeun Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Decision Making ◽  
Coronary Artery ◽  
Learning Algorithm ◽  
Flow Characteristics ◽  
Machine Learning Algorithm ◽  
Decision Algorithm ◽  
Computational Fluid Dynamics Cfd ◽  
Biometric Features

A two-step machine learning (ML) algorithm for coronary artery decision making is introduced, to increase the data quality by providing flow characteristics and biometric features by aid of computational fluid dynamics (CFD).

scholarly journals Coronary CT Angiography–derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling

Radiology ◽  
2018 ◽  
Vol 288 (1) ◽  
pp. 64-72 ◽  
Author(s):  
Christian Tesche ◽  
Carlo N. De Cecco ◽  
Stefan Baumann ◽  
Matthias Renker ◽  
Tindal W. McLaurin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fluid Dynamics ◽  
Coronary Ct Angiography ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Dynamics Modeling ◽  
Fractional Flow ◽  
Computational Fluid Dynamics Modeling ◽  
Coronary Ct ◽  
Flow Reserve

Decision Making by a Multiple-Rule Classifier

2011 ◽  
pp. 176-183
Author(s):  
Ivan Bruha
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Knowledge Discovery ◽  
Learning Algorithm ◽  
Decision Rules ◽  
Mode Decision ◽  
Machine Learning Algorithm ◽  
Statistical Tools ◽  
Conflict Situations ◽  
Decision Supporting

A rule-inducing learning algorithm yields a set of decision rules that depict knowledge discovered from a (usually large) dataset; therefore, this topic is often known as knowledge discovery from databases (KDD). Any classifier (or, expect system) then can utilize this decision set to derive a decision about given problems, observations, or diagnostics. The decision set (induced by a learning algorithm) may be either of the form of an ordered or unordered set of rules. The latter seems to be more understandable by humans and directly applicable in most expert systems, or generally, any decision- supporting one. However, classification utilizing the unordered-mode decision set may be accompanied by some conflict situations, particularly when several rules belonging to different classes match (are satisfied by, “fire” for) an input to-be-classified (unseen) object. One of the possible solutions to this conflict is to associate each decision rule induced by a learning algorithm with a numerical factor, which is commonly called the rule quality (An & Cercone, 2001; Bergadano et al., 1988; Bruha, 1997; Kononenko, 1992; Mingers, 1989; Tkadlec & Bruha, 2003). This article first briefly introduces the underlying principles for defining rules qualities, including statistical tools such as contingency tables and then surveys empirical and statistical formulas of the rule quality and compares their characteristics. Afterwards, it presents an application of a machine learning algorithm utilizing various formulas of the rule qualities in medical area.

MAITS

2011 ◽  
pp. 189-208 ◽  
Author(s):  
Dharmendra Sharma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Learning Algorithm ◽  
Computer Forensics ◽  
The Other ◽  
Security Requirements ◽  
Machine Learning Algorithm ◽  
Agent Based ◽  
Authentication And Authorization ◽  
Multi Agent

In this chapter, we propose a multi-agent-based information technology (IT) security approach (MAITS) as a holistic solution to the increasing needs of securing computer systems. Each specialist task for security requirements is modeled as a specialist agent. MAITS has five groups of working agents—administration assistant agents, authentication and authorization agents, system log *monitoring agents, intrusion detection agents, and pre-mortem-based computer forensics agents. An assessment center, which is comprised of yet another special group of agents, plays a key role in coordinating the interaction of the other agents. Each agent has an agent engine of an appropriate machine-learning algorithm. The engine enables the agent with learning, reasoning, and decision-making abilities. Each agent also has an agent interface, through which the agent interacts with other agents and also the environment.

Machine learning algorithm-based risk prediction model of coronary artery disease

2018 ◽  
Vol 45 (5) ◽  
pp. 901-910 ◽  
Author(s):  
Shaik Mohammad Naushad ◽  
Tajamul Hussain ◽  
Bobbala Indumathi ◽  
Khatoon Samreen ◽  
Salman A. Alrokayan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Prediction Model ◽  
Risk Prediction ◽  
Learning Algorithm ◽  
Risk Prediction Model ◽  
Machine Learning Algorithm ◽  
Artery Disease
Sign in / Sign up

Export Citation Format

Share Document