Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms
To categorize patient diagnosis data related to Chronic Kidney Disease (CKD), this study compares the classification performance of Support Vector Machines (SVM) enhanced by Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). CKD is a severe illness in which the kidneys fail to adequately...
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| Format: | Article |
| Language: | English |
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Mathematics Department UIN Maulana Malik Ibrahim Malang
2024-11-01
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| Series: | Cauchy: Jurnal Matematika Murni dan Aplikasi |
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| Online Access: | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/29320 |
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| author | Qonita Ilmi Awalin Ika Hesti Agustin Alfian Futuhul Hadi Dafik Dafik R. Sunder |
| author_facet | Qonita Ilmi Awalin Ika Hesti Agustin Alfian Futuhul Hadi Dafik Dafik R. Sunder |
| author_sort | Qonita Ilmi Awalin |
| collection | DOAJ |
| description | To categorize patient diagnosis data related to Chronic Kidney Disease (CKD), this study compares the classification performance of Support Vector Machines (SVM) enhanced by Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). CKD is a severe illness in which the kidneys fail to adequately filter blood and perform their normal functions. This study utilized secondary data consisting of patient conditions and health information. Based on references from CKD-related journals, 15 independent variables and one dependent variable were selected from an initial set of 54 variables. To address the issue of unbalanced data, an oversampling technique was applied, and the data was subsequently split into 80% for training and 20% for testing. During the training phase, SVM-PSO and SVM-GA models were developed, and the gamma value was optimized using the RBF kernel function of SVM. The results indicated that in classifying CKD patient diagnosis data, the SVM-PSO model (97.54% accuracy) outperformed the SVM-GA model (97.37% accuracy). This finding suggests that PSO-based hyperparameter optimization yields a superior model for data classification |
| format | Article |
| id | doaj-art-17fae305a593438e8e021ee507fd2ca0 |
| institution | OA Journals |
| issn | 2086-0382 2477-3344 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Mathematics Department UIN Maulana Malik Ibrahim Malang |
| record_format | Article |
| series | Cauchy: Jurnal Matematika Murni dan Aplikasi |
| spelling | doaj-art-17fae305a593438e8e021ee507fd2ca02025-08-20T02:13:45ZengMathematics Department UIN Maulana Malik Ibrahim MalangCauchy: Jurnal Matematika Murni dan Aplikasi2086-03822477-33442024-11-019232032810.18860/ca.v9i2.293208270Optimizing Data Classification in Support Vector Machines Using Metaheuristic AlgorithmsQonita Ilmi Awalin0Ika Hesti Agustin1Alfian Futuhul Hadi2Dafik Dafik3R. Sunder4Universitas JemberUniversitas JemberUniversitas JemberUniversitas JemberSchool of Computer Science and Engineering, Galgotias UniversityTo categorize patient diagnosis data related to Chronic Kidney Disease (CKD), this study compares the classification performance of Support Vector Machines (SVM) enhanced by Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). CKD is a severe illness in which the kidneys fail to adequately filter blood and perform their normal functions. This study utilized secondary data consisting of patient conditions and health information. Based on references from CKD-related journals, 15 independent variables and one dependent variable were selected from an initial set of 54 variables. To address the issue of unbalanced data, an oversampling technique was applied, and the data was subsequently split into 80% for training and 20% for testing. During the training phase, SVM-PSO and SVM-GA models were developed, and the gamma value was optimized using the RBF kernel function of SVM. The results indicated that in classifying CKD patient diagnosis data, the SVM-PSO model (97.54% accuracy) outperformed the SVM-GA model (97.37% accuracy). This finding suggests that PSO-based hyperparameter optimization yields a superior model for data classificationhttps://ejournal.uin-malang.ac.id/index.php/Math/article/view/29320chronic kidney diseaseclassificationsvm-psosvm-ga |
| spellingShingle | Qonita Ilmi Awalin Ika Hesti Agustin Alfian Futuhul Hadi Dafik Dafik R. Sunder Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms Cauchy: Jurnal Matematika Murni dan Aplikasi chronic kidney disease classification svm-pso svm-ga |
| title | Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms |
| title_full | Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms |
| title_fullStr | Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms |
| title_full_unstemmed | Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms |
| title_short | Optimizing Data Classification in Support Vector Machines Using Metaheuristic Algorithms |
| title_sort | optimizing data classification in support vector machines using metaheuristic algorithms |
| topic | chronic kidney disease classification svm-pso svm-ga |
| url | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/29320 |
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