Improvement of Kernel SVM to Enhance Accuracy in Chronic Kidney Disease

Authors

  • Ganda Wijaya Universitas Nusa Mandiri

DOI:

10.33395/sinkron.v9i1.13112

Keywords:

Chronic Kidney Disease Data Mining, Kernel SVM, Support Vector Machine, Particle Swarm Optimization

Abstract

Chronic Kidney Disease (CKD) is a highly serious health issue, affecting millions of people worldwide. Early diagnosis and accurate prediction of chronic kidney disease are key factors in successful treatment. One of the approaches used for diagnosing this disease is through machine learning algorithms, specifically the Support Vector Machine (SVM) method. By collecting CKD data that includes various clinical parameters, initial kernel selection as well as various kernels are tested. However, the accuracy of the SVM method can be further improved for better diagnosis. The objective of this research is to enhance accuracy, optimize parameters, and improve the SVM kernel by incorporating the Particle Swarm Optimization (PSO) algorithm. The results of this study indicate that the use of PSO method to improve SVM kernels can significantly enhance accuracy in CKD diagnosis compared to conventional SVM approaches, potentially aiding medical practitioners in early disease diagnosis and better CKD management, which in turn can improve patient prognosis and quality of life

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References

Akben, S. B. (2018). Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis, Blood Analysis and Disease History. Irbm, 39(5), 353–358. https://doi.org/10.1016/j.irbm.2018.09.004

Aqlan, F., Markle, R., & Shamsan, A. (2017). Data mining for chronic kidney disease prediction. 67th Annual Conference and Expo of the Institute of Industrial Engineers 2017, (March), 1789–1794.

Ariyati, I., Rosyida, S., Ramanda, K., Riyanto, V., Faizah, S., & Ridwansyah. (2020). Optimization of the Decision Tree Algorithm Used Particle Swarm Optimization in the Selection of Digital Payments. Journal of Physics: Conference Series, 1641(1). https://doi.org/10.1088/1742-6596/1641/1/012090

Ariyati, Indah, Ridwansyah, & Suhardjono. (2018). Implementasi Particle Swarm Optimization untuk Optimalisasi Data Mining Dalam Evaluasi Kinerja Asisten Dosen. JIKO (Jurnal Informatika Dan Komputer) STMIK AKAKOM, 3(2), 70–75.

Boukenze, B., Haqiq, A., & Mousannif, H. (2017). Predicting chronic kidney failure disease using data mining techniques. Lecture Notes in Electrical Engineering, 397, 701–712. https://doi.org/10.1007/978-981-10-1627-1_55

Gharibdousti, M. S., Azimi, K., Hathikal, S., & Won, D. H. (2017). Prediction of Chronic Kidney Disease Using Data Mining Techniques. Proceedings of the 2017 Industrial and Systems Engineering Conference, 2135–2140.

Kaur, G., & Tech, M. (2017). Mining Algorithms In Hadoop. (Icici).

Kunwar, V., Chandel, K., Sabitha, A. S., & Bansal, A. (2016). Chronic Kidney Disease analysis using data mining classification techniques. Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016, 300–305. https://doi.org/10.1109/CONFLUENCE.2016.7508132

Purwaningsih, E. (2022). Improving the Performance of Support Vector Machine With Forward Selection for Prediction of Chronic Kidney Disease. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 8(1), 18–24. https://doi.org/10.33480/jitk.v8i1.3327

Rady, E. H. A., & Anwar, A. S. (2019). Prediction of kidney disease stages using data mining algorithms. Informatics in Medicine Unlocked, 15(December 2018), 100178. https://doi.org/10.1016/j.imu.2019.100178

Rezayi, S., Maghooli, K., & Saeedi, S. (2021). Applying Data Mining Approaches for Chronic Kidney Disease Diagnosis. International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING.

Ridwansyah, Ariyati, I., & Faizah, S. (2018). PARTICLE SWARM OPTIMIZATION BERBASIS CO-EVOLUSIONER DALAM EVALUASI KINERJA ASISTEN DOSEN. Jurnal SAINTEKOM, 9(2), 166–177. https://doi.org/https://doi.org/10.33020/saintekom.v9i2.96

Riyanto, V., Hamid, A., & Ridwansyah. (2019). Prediction of Student Graduation Time Using the Best Algorithm. Indonesian Journal of Artificial Intelligence and Data Mining, 2(2), 1–9. https://doi.org/http://dx.doi.org/10.24014/ijaidm.v2i1.6424

Vijayarani, S., & Dhayanand, S. (2015). Data Mining Classification Algorithms for Kidney Disease Prediction. International Journal on Cybernetics & Informatics, 4(4), 13–25. https://doi.org/10.5121/ijci.2015.4402

Zeynu, S. (2018). Prediction of Chronic Kidney Disease Using Data Mining Feature Selection and Ensemble Method. WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATION, 15, 168–176.

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How to Cite

Wijaya, G. (2024). Improvement of Kernel SVM to Enhance Accuracy in Chronic Kidney Disease. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 136-144. https://doi.org/10.33395/sinkron.v9i1.13112