• Syaiful Anam Computer and Data Science Laboratory, Mathematics Department, Brawijaya University, Indonesia
  • Abdi Negara Guci Mathematics Study Program, Mathematics Department, Brawijaya University, Indonesia
  • Fery Widhiatmoko Actuarial Science Study Program, Mathematics Department, Brawijaya University, Indonesia
  • Mila Kurniawaty Actuarial Science Study Program, Mathematics Department, Brawijaya University, Indonesia
  • Komang Agus Arta Wijaya Actuarial Science Study Program, Mathematics Department, Brawijaya University, Indonesia
Keywords: Claim prediction, HEALTH INSURANCE, Support Vector Regression, Bat Algorithm, Parameter Selection


Health insurance industry is very much needed by the community in handling the financial risks in the health sector. The number of claims greatly affects the achievement of profits and the sustainability of the health insurance industry. Therefore, filing claims by insurance users from year to year is important to be predicted in insurance firm. The Machine Learning (ML) method promises to be a good solution for predicting health insurance claims compared to conventional data analytics methods. Support Vector Machine (SVM) is one of the superior ML approaches. Nonetheless, SVM performance is controlled by the suitable selection of SVM parameters. The SVM parameters is typically selected by trial and error, sometimes resulting in not optimal performance and taking a long time to complete. Swarm intelligence-based algorithms can be used to select the best parameters from SVM. This method is capable of locating the global best solution, is simple to implemented, and doesn't involve derivatives. One of the best swarm intelligence algorithms is the Bat Algorithm (BA). BA has a faster convergence rate than other algorithms, for example Particle Swarm Optimization (PSO). Based on this situation, this paper offers the new classification model for predicting health insurance claim based on SVM and BA. The metrics utilized for evaluation are accuracy, recall, precision, f1-score, and computing time. The experimental outcomes show that the proposed approach is superior to the conventional SVM and the hybrid of SVM and PSO in forecasting health insurance claims. In addition, the proposed method has a substantially shorter computing time than the hybrid of SVM and PSO. The outcomes of the experiments also indicate that the new classification model for predicting health insurance claim based on the SVM and BA can avoid over-fitting condition.


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M. hanafy and O. M. A. Mahmoud, “Predict Health Insurance Cost by using Machine Learning and DNN Regression Models,” International Journal of Innovative Technology and Exploring Engineering, vol. 10, no. 3, pp. 137–143, Jan. 2021, doi: 10.35940/ijitee.C8364.0110321.

R. R. Bovbjerg and J. Hadley, “Why Health Insurance Is Important,” Health Policy Briefs, vol. 11, pp. 1–3, 2007, [Online]. Available:

T. Hwang and B. Greenford, “Across-section Analysis of the Determinants of Life Insurance Consumption in Mainland China, Hong Kong, and Taiwan,” Risk Management and Insurance Review, vol. 8, no. 1, pp. 103–125, 2005.

M. Bärtl and S. Krummaker, “Prediction of claims in export credit finance: a comparison of four machine learning techniques,” Risks, vol. 8, no. 1, Mar. 2020, doi: 10.3390/risks8010022.

S. Goundar, S. Prakash, P. Sadal, and A. Bhardwaj, “Health Insurance Claim Prediction Using Artificial Neural Networks,” International Journal of System Dynamics Applications, vol. 9, no. 3, pp. 40–57, Jun. 2020, doi: 10.4018/ijsda.2020070103.

K. A. Smith, R. J. Willis, and M. Brooks, “An Analysis of Customer Retention and Insurance Claim Patterns Using Data Mining: A Case Study,” Source: The Journal of the Operational Research Society, vol. 51, no. 5, pp. 532–541, 2000.

Z. Quan and E. A. Valdez, “Predictive analytics of insurance claims using multivariate decision trees,” Dependence Modeling, vol. 6, no. 1, pp. 377–407, Dec. 2018, doi: 10.1515/demo-2018-0022.

E. Alamir, T. Urgessa, A. Hunegnaw, and T. Gopikrishna, “Motor Insurance Claim Status Prediction using Machine Learning Techniques,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 12, no. 3, p. 2021, [Online]. Available:

H. Imaduddin, B. Aditya Hermansyah, and F. B. Aura Salsabilla, “Comparison of Support Vector Machine and Decision Tree Methods in the Classification of Breast Cancer,” Jurnal Pendidikan Teknologi Informasi, vol. 5, pp. 22–30, 2021.

R. Kusumawati, A. D’Arofah, and P. A. Pramana, “Comparison Performance of Naive Bayes Classifier and Support Vector Machine Algorithm for Twitter’s Classification of Tokopedia Services,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Nov. 2019. doi: 10.1088/1742-6596/1320/1/012016.

E. A. Zanaty, “Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification,” Egyptian Informatics Journal, vol. 13, no. 3, pp. 177–183, 2012, doi: 10.1016/j.eij.2012.08.002.

D. L. Olson and D. Delen, Advanced Data Mining Techniques. Berlin Heidelberg: Springer-Verlag , 2008.

N. Çolakoğlu and B. Akkaya, “Comparison of Multi-class Classification Algorithms on Early Diagnosis of Heart Diseases Recommendation System for Spotify View project,” in Proceeding of Conference: Recent Advances in Data Science and Business Analytics, 2019, pp. 162–171. [Online]. Available:

D. N. Avianty, Prof. I. G. P. S. Wijaya, and F. Bimantoro, “The Comparison of SVM and ANN Classifier for COVID-19 Prediction,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 13, no. 2, p. 128, Aug. 2022, doi: 10.24843/lkjiti.2022.v13.i02.p06.

B. K. R, B. K, hreyas M. S, onu S, A. H, and A. B. J, “SVM Based Plant Diseases Detection using Image Processing,” International Journal of Computer Sciences and Engineering, vol. 7, no. 5, pp. 1263–1266, May 2019, doi: 10.26438/ijcse/v7i5.12631266.

M. Hussein and A. H. Abbas, “Plant Leaf Disease Detection Using Support Vector Machine,” Al-Mustansiriyah Journal of Science, vol. 30, no. 1, pp. 105–110, Aug. 2019, doi: 10.23851/mjs.v30i1.487.

N. Tripathy, “Stock Price Prediction Using Support Vector Machine Approach,” in International Academic Conference on Management & Economics, 2019, pp. 44–59.

A. A. Hasseim, R. Sudirman, and P. I. Khalid, “Handwriting Classification Based on Support Vector Machine with Cross Validation,” Engineering, vol. 05, no. 05, pp. 84–87, 2013, doi: 10.4236/eng.2013.55b017.

N. K. Gyamfi and J. D. Abdulai, “Bank Fraud Detection Using Support Vector Machine,” in 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, Institute of Electrical and Electronics Engineers Inc., Jan. 2019, pp. 37–41. doi: 10.1109/IEMCON.2018.8614994.

D. Zhang, B. Bhandari, and D. Black, “Credit Card Fraud Detection Using Weighted Support Vector Machine,” Appl Math (Irvine), vol. 11, no. 12, pp. 1275–1291, 2020, doi: 10.4236/am.2020.1112087.

Z. Rustam and A. A. Ruvita, “Application Support Vector Machine on Face Recognition for Gender Classification,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Dec. 2018. doi: 10.1088/1742-6596/1108/1/012067.

S. Sulistiana and M. A. Muslim, “Support Vector Machine (SVM) Optimization Using Grid Search and Unigram to Improve E-Commerce Review Accuracy,” Journal of Soft Computing Exploration, vol. 1, no. 1, pp. 8–15, 2020.

W. M. Czarnecki, S. Podlewska, and A. J. Bojarski, “Robust optimization of SVM hyperparameters in the classification of bioactive compounds,” J Cheminform, vol. 7, no. 1, Aug. 2015, doi: 10.1186/s13321-015-0088-0.

W. Dubitzky, O. Wolkenhauer, K.-H. Cho, and H. Yokota, Encyclopedia of Systems Biology. London: Springer International Publishing, 2013. doi: 10.1007/978-1-4419-9863-7.

A. K. Kordon, Applying computational intelligence: How to create value. Springer Berlin Heidelberg, 2010. doi: 10.1007/978-3-540-69913-2.

S. Anam and Z. Fitriah, “Early Blight Disease Segmentation on Tomato Plant Using K-means Algorithm with Swarm Intelligence-based Algorithm,” Int J Math Comput Sci, vol. 16, no. 4, pp. 1217–1228, 2021, [Online]. Available:

S. Anam, M. R. A. Putra, Z. Fitriah, I. Yanti, N. Hidayat, and D. M. Mahanani, “Health Claim Insurance Prediction Using Support Vector Machine with Particle Swarm Optimization,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 2, pp. 0797–0806, Jun. 2023, doi: 10.30598/barekengvol17iss2pp0797-0806.

X.-S. Yang, “A New Metaheuristic Bat-Inspired Algorithm,” Apr. 2010. [Online]. Available:

How to Cite
S. Anam, A. Guci, F. Widhiatmoko, M. Kurniawaty, and K. Wijaya, “DEVELOPMENT OF HEALTH INSURANCE CLAIM PREDICTION METHOD BASED ON SUPPORT VECTOR MACHINE AND BAT ALGORITHM”, BAREKENG: J. Math. & App., vol. 17, no. 4, pp. 2281-2292, Dec. 2023.