A Machine Learning Framework for Feature Selection in Heart Disease Classification Using Improved Particle Swarm Optimization with Support Vector Machine Classifier

Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solut...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Programming and computer software Jg. 44; H. 6; S. 388 - 397
Hauptverfasser: Vijayashree, J., Sultana, H. Parveen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Moscow Pleiades Publishing 01.11.2018
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Cardiovascular disease
Classification
Classifiers
Computer Science
Datasets
Genetic algorithms
Heart diseases
Heuristic methods
Machine learning
Medical records
Operating Systems
Particle swarm optimization
Performance evaluation
Software Engineering
Software Engineering/Programming and Operating Systems
Support systems
Support vector machines
population diversity function
tuning function
Support Vector Machine
fitness function
ROC analysis
Particle Swarm Optimization
ISSN:0361-7688, 1608-3261
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solution of the given problem. Hence, there is a need to use an effective feature selection algorithm for finding the more important features that contribute more in diagnosing the diseases. The Particle Swarm Optimization (PSO) is one of the metaheuristic algorithms to find the best solution with less time. Nowadays, PSO algorithm is not only used to select the more significant features but also removes the irrelevant and redundant features present in the dataset. However, the traditional PSO algorithm has an issue in selecting the optimal weight to update the velocity and position of the particles. To overcome this issue, this paper presents a novel function for identifying optimal weights on the basis of population diversity function and tuning function. We have also proposed a novel fitness function for PSO with the help of Support Vector Machine (SVM). The objective of the fitness function is to minimize the number of attributes and increase the accuracy. The performance of the proposed PSO-SVM is compared with the various existing feature selection algorithms such as Info gain, Chi-squared, One attribute based, Consistency subset, Relief, CFS, Filtered subset, Filtered attribute, Gain ratio and PSO algorithm. The SVM classifier is also compared with several classifiers such as Naive Bayes, Random forest and MLP.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0361-7688
1608-3261
DOI:10.1134/S0361768818060129