A novel Swarm Optimized Clustering based genetic algorithm for medical decision support system
The clinical and diagnostic levels of medical decision-making may benefit from use of machine learning techniques. Algorithms for feature selection provide an basis about machine learning. The most discriminating health-related traits from the initial feature set may be quickly and effectively ident...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423001575 |
| _version_ | 1797780657315774464 |
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| author | K. Uma K. Perumal |
| author_facet | K. Uma K. Perumal |
| author_sort | K. Uma |
| collection | DOAJ |
| description | The clinical and diagnostic levels of medical decision-making may benefit from use of machine learning techniques. Algorithms for feature selection provide an basis about machine learning. The most discriminating health-related traits from the initial feature set may be quickly and effectively identified in a medical context by using feature selection. Choosing the most relevant attributes of data classes and improving classification performance are the two main goals of feature selection algorithms. The process of feature selection not only identifies the most useful characteristics but also aids in lowering the dataset's overall dimensions. Hence in this article, we propose a novel algorithm based on machine learning. Initially, the dataset is collected and preprocessed using normalisation method. The features are extracted using Linear Discriminant Analysis (LDA) and the relevant features are selected using the proposed Swarm Optimized Clustering based Genetic Algorithm (SOC-GA). On the healthcare datasets, we implemented the suggested method into action. Using Random Forest (RF) and Support Vector Machine (SVM) classifiers, performances of chosen feature subsets are assessed based on accuracy. The empirical findings from our studies in this research are competitive in terms of accuracy and outperformed the other well-known feature selection methods. This research offers remedies that might improve the accurate, efficient and trustworthy decision-making process in healthcare systems for targeted medical treatments. |
| first_indexed | 2024-03-12T23:47:05Z |
| format | Article |
| id | doaj.art-136f8b7504ed4303a15af3794b04b868 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-12T23:47:05Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-136f8b7504ed4303a15af3794b04b8682023-07-14T04:28:25ZengElsevierMeasurement: Sensors2665-91742023-08-0128100821A novel Swarm Optimized Clustering based genetic algorithm for medical decision support systemK. Uma0K. Perumal1Department of Computer Science, Yadava College Madurai, India; Corresponding author.Department of Computer Applications, School of Information Technology, Madurai Kamaraj University, Madurai, IndiaThe clinical and diagnostic levels of medical decision-making may benefit from use of machine learning techniques. Algorithms for feature selection provide an basis about machine learning. The most discriminating health-related traits from the initial feature set may be quickly and effectively identified in a medical context by using feature selection. Choosing the most relevant attributes of data classes and improving classification performance are the two main goals of feature selection algorithms. The process of feature selection not only identifies the most useful characteristics but also aids in lowering the dataset's overall dimensions. Hence in this article, we propose a novel algorithm based on machine learning. Initially, the dataset is collected and preprocessed using normalisation method. The features are extracted using Linear Discriminant Analysis (LDA) and the relevant features are selected using the proposed Swarm Optimized Clustering based Genetic Algorithm (SOC-GA). On the healthcare datasets, we implemented the suggested method into action. Using Random Forest (RF) and Support Vector Machine (SVM) classifiers, performances of chosen feature subsets are assessed based on accuracy. The empirical findings from our studies in this research are competitive in terms of accuracy and outperformed the other well-known feature selection methods. This research offers remedies that might improve the accurate, efficient and trustworthy decision-making process in healthcare systems for targeted medical treatments.http://www.sciencedirect.com/science/article/pii/S2665917423001575Medical decision-makingMachine learningLinear discriminant analysis (LDA)Swarm optimized clustering based genetic algorithm (SOC-GA)Random forest (RF)Support vector machine (SVM) |
| spellingShingle | K. Uma K. Perumal A novel Swarm Optimized Clustering based genetic algorithm for medical decision support system Measurement: Sensors Medical decision-making Machine learning Linear discriminant analysis (LDA) Swarm optimized clustering based genetic algorithm (SOC-GA) Random forest (RF) Support vector machine (SVM) |
| title | A novel Swarm Optimized Clustering based genetic algorithm for medical decision support system |
| title_full | A novel Swarm Optimized Clustering based genetic algorithm for medical decision support system |
| title_fullStr | A novel Swarm Optimized Clustering based genetic algorithm for medical decision support system |
| title_full_unstemmed | A novel Swarm Optimized Clustering based genetic algorithm for medical decision support system |
| title_short | A novel Swarm Optimized Clustering based genetic algorithm for medical decision support system |
| title_sort | novel swarm optimized clustering based genetic algorithm for medical decision support system |
| topic | Medical decision-making Machine learning Linear discriminant analysis (LDA) Swarm optimized clustering based genetic algorithm (SOC-GA) Random forest (RF) Support vector machine (SVM) |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423001575 |
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