Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO
DOI:
https://doi.org/10.31294/p.v26i1.3282Keywords:
K-Nearest Neighbors, Predictive Model, Chronic Kidney Disease, Particle Swarm OptimizationAbstract
This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.
References
Alaiad, A., Najadat, H., Mohsen, B., & Balhaf, K. (2020). Classification and Association Rule Mining Technique for Predicting Chronic Kidney Disease. Journal of Information and Knowledge Management, 19(1). https://doi.org/10.1142/S0219649220400158
Arif-Ul-Islam, & Ripon, S. H. (2019). Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree. 2nd International Conference on Electrical, Computer and Communication Engineering, ECCE 2019, 1–6. https://doi.org/10.1109/ECACE.2019.8679388
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
Iqbal, M., Herliawan, I., Ridwansyah, Gata, W., Hamid, A., Purnama, J. J., & Yudhistira. (2020). Implementation of Particle Swarm Optimization Based Machine Learning Algorithm for Student Performance Prediction. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 6(2), 195–204. https://doi.org/10.33480/jitk.v6i2.1695
Larose, D. T., & Larose, C. D. (2015). Data Mining And Predictive Analytics. John Wiley and Sons. ISBN: 978-1-118-11619-7
Pramanik, R., Khare, S., & Gourisaria, M. K. (2021). Inferring the Occurrence of Chronic Kidney Failure: A Data Mining Solution. Proceedings of Second Doctoral Symposium on Computational Intelligence. https://doi.org/https://doi.org/10.1007/978-981-16-3346-1_59
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
Nurdin, H., Sartini, S., Sumarna, S., Maulana, Y. I., & Riyanto, V. (2023). Prediction of Student Graduation with the Neural Network Method Based on Particle Swarm Optimization. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 8(4), 2353–2362.https://doi.org/10.33395/sinkron.v8i4.12973
Rady, E. H. A., & Anwar, A. S. (2019). Prediction of kidney disease stages using data mining algorithms. Informatics in Medicine Unlocked, 15(December 2019), 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. https://doi.org/10.18201/ijisae.2021473640
Ridwansyah, R., Riyanto, V., Hamid, A., Rahayu, S., & Purnama, J. J. (2022).
Grouping Data in Predicting Infant Mortality Using K-Means and Decision Tree. Paradigma, 24(2), 168–174. https://doi.org/10.31294/paradigma.v24i2.1399
Ridwansyah, R., Wijaya, G., & Purnama, J. J. (2020). Hybrid Optimization Method Based on Genetic Algorithm for Graduates Students. Jurnal Pilar Nusa Mandiri, 16(1), 53–58. https://doi.org/10.33480/pilar.v16i1.1180
Saha, I., Gourisaria, M. K., & Harshvardhan, G. M. (2022). Classification System for Prediction of Chronic Kidney Disease Using Data Mining Techniques. Lecture Notes in Networks and Systems, 318(May 2017), 429–443. https://doi.org/10.1007/978-981-16-5689-7_38
Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. https://doi.org/https://doi.org/10.1007/s42979-021-00592-x.
Wijaya, G. (2024). Improvement of Kernel SVM to Enhance Accuracy in Chronic Kidney Disease. 9(1), 136–144. https://doi.org/https://doi.org/10.33395/sinkron.v9i1.13112 e-ISSN
Witten, I. H. (2017). DATA MINING (Fourth Edition). Elsevier. ISBN: 9780128042915.
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