HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION
The number of claims plays an important role the profit achievement of health insurance companies. Prediction of the number of claims could give the significant implications in the profit margins generated by the health insurance company. Therefore, the prediction of claim submission by insurance use...
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Universitas Pattimura
2023-06-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7788 |
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| author | Syaiful Anam M. Rafael Andika Putra Zuraidah Fitriah Indah Yanti Noor Hidayat Dwi Mifta Mahanani |
| author_facet | Syaiful Anam M. Rafael Andika Putra Zuraidah Fitriah Indah Yanti Noor Hidayat Dwi Mifta Mahanani |
| author_sort | Syaiful Anam |
| collection | DOAJ |
| description | The number of claims plays an important role the profit achievement of health insurance companies. Prediction of the number of claims could give the significant implications in the profit margins generated by the health insurance company. Therefore, the prediction of claim submission by insurance users in that year needs to be done by insurance companies. Machine learning methods promise the great solution for claim prediction of the health insurance users. There are several machine learning methods that can be used for claim prediction, such as the Naïve Bayes method, Decision Tree (DT), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The previous studies show that the SVM has some advantages over the other methods. However, the performance of the SVM is determined by some parameters. Parameter selection of SVM is normally done by trial and error so that the performance is less than optimal. Some optimization algorithms based heuristic optimization can be used to determine the best parameter values of SVM, for example Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). They are able to search the global optimum, easy to be implemented. The derivatives aren’t needed in its computation. Several researches show that PSO give the better solutions if it is compared with GA. All particles in the PSO are able to find the solution near global optimal. For these reasons, this article proposes the health claim insurance prediction using SVM with PSO. The experimental results show that the SVM with PSO gives the great performance in the health claim insurance prediction and it has been proven that the SVM with PSO give better performance than the SVM standard. |
| format | Article |
| id | doaj-art-91a409ff74ee4bc1b63dae602a4d514a |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-91a409ff74ee4bc1b63dae602a4d514a2025-08-20T04:00:55ZengUniversitas PattimuraBarekeng1978-72272615-30172023-06-011720797080610.30598/barekengvol17iss2pp0797-08067788HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATIONSyaiful Anam0M. Rafael Andika Putra1Zuraidah Fitriah2Indah Yanti3Noor Hidayat4Dwi Mifta Mahanani5Computer and Data Science Laboratory, Mathematics Department, Brawijaya University, IndonesiaComputer and Data Science Laboratory, Mathematics Department, Brawijaya University, IndonesiaMathematical Optimization and Computing Research Group, Brawijaya University, IndonesiaComputer and Data Science Laboratory, Mathematics Department, Brawijaya University, IndonesiaMathematical Optimization and Computing Research Group, Brawijaya University, IndonesiaMathematical Optimization and Computing Research Group, Brawijaya University, IndonesiaThe number of claims plays an important role the profit achievement of health insurance companies. Prediction of the number of claims could give the significant implications in the profit margins generated by the health insurance company. Therefore, the prediction of claim submission by insurance users in that year needs to be done by insurance companies. Machine learning methods promise the great solution for claim prediction of the health insurance users. There are several machine learning methods that can be used for claim prediction, such as the Naïve Bayes method, Decision Tree (DT), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The previous studies show that the SVM has some advantages over the other methods. However, the performance of the SVM is determined by some parameters. Parameter selection of SVM is normally done by trial and error so that the performance is less than optimal. Some optimization algorithms based heuristic optimization can be used to determine the best parameter values of SVM, for example Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). They are able to search the global optimum, easy to be implemented. The derivatives aren’t needed in its computation. Several researches show that PSO give the better solutions if it is compared with GA. All particles in the PSO are able to find the solution near global optimal. For these reasons, this article proposes the health claim insurance prediction using SVM with PSO. The experimental results show that the SVM with PSO gives the great performance in the health claim insurance prediction and it has been proven that the SVM with PSO give better performance than the SVM standard.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7788health insuranceclaim predictionsupport vector machineparticle swarm optimizationparameter’s selectionglobal optimum |
| spellingShingle | Syaiful Anam M. Rafael Andika Putra Zuraidah Fitriah Indah Yanti Noor Hidayat Dwi Mifta Mahanani HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION Barekeng health insurance claim prediction support vector machine particle swarm optimization parameter’s selection global optimum |
| title | HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION |
| title_full | HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION |
| title_fullStr | HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION |
| title_full_unstemmed | HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION |
| title_short | HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION |
| title_sort | health claim insurance prediction using support vector machine with particle swarm optimization |
| topic | health insurance claim prediction support vector machine particle swarm optimization parameter’s selection global optimum |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7788 |
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