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...
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| Published in: | Programming and computer software Vol. 44; no. 6; pp. 388 - 397 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Moscow
Pleiades Publishing
01.11.2018
Springer Nature B.V |
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| ISSN: | 0361-7688, 1608-3261 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Sultana, H. Parveen Vijayashree, J. |
| Author_xml | – sequence: 1 givenname: J. surname: Vijayashree fullname: Vijayashree, J. email: vijayashree.j@vit.ac.in organization: School of Computer Science and Engineering, Vellore Institute of Technology – sequence: 2 givenname: H. Parveen surname: Sultana fullname: Sultana, H. Parveen organization: School of Computer Science and Engineering, Vellore Institute of Technology |
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| Keywords | population diversity function tuning function Support Vector Machine fitness function ROC analysis Particle Swarm Optimization |
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| SubjectTerms | 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 |
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| Title | A Machine Learning Framework for Feature Selection in Heart Disease Classification Using Improved Particle Swarm Optimization with Support Vector Machine Classifier |
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