Genetic Algorithm for Feature Selection and Parameter Optimization to Enhance Learning on Framingham Heart Disease Dataset

2020 ◽  
pp. 11-25
Author(s):  
Shiwani Gupta ◽  
R. R. Sedamkar
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Heart Disease ◽  
Parameter Optimization

An Effective Classification Algorithm for Heart Disease Prediction with Genetic Algorithm for Feature Selection

2021 ◽  
Author(s):  
Samina Kanwal ◽  
Junaid Rashid ◽  
Muhammad Wasif Nisar ◽  
Jungeun Kim ◽  
Amir Hussain
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Heart Disease ◽  
Classification Algorithm ◽  
Disease Prediction

Diagnosis of coronary heart disease based on1H NMR spectra of human blood plasma using genetic algorithm-based feature selection

2013 ◽  
Vol 27 (10) ◽  
pp. 318-322 ◽  
Author(s):  
Mahdi Vasighi ◽  
Ali Zahraei ◽  
Saeed Bagheri ◽  
Jamshid Vafaeimanesh
Keyword(s):  
Genetic Algorithm ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Blood Plasma ◽  
Human Blood ◽  
Nmr Spectra ◽  
Human Blood Plasma

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

Machining Parameter Optimization of Bidirectional CFRP Composite Pipe by Genetic Algorithm

2014 ◽  
Vol 56 (9) ◽  
pp. 728-736 ◽  
Author(s):  
Krishnasamy Vijaykumar ◽  
Kavan Panneerselvam ◽  
Abdullah Naveen Sait
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Composite Pipe ◽  
Cfrp Composite ◽  
Machining Parameter
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