Improving the Accuracy of Heart Failure Prediction Using the Particle Swarm Optimization Method

Authors

  • Dewi Yuliandari Universitas Bina Sarana Informatika
  • Yudhistira Universitas Bina Sarana Informatika
  • Anus Wuryanto Universitas Bina Sarana Informatika
  • Sidik Sidik Universitas Nusa Mandiri
  • Findi Ayu Sariasih Universitas Nusa Mandiri

DOI:

10.33395/sinkron.v9i1.13017

Keywords:

Data Mining; Heart Failure; Improve Accuracy; Neural Network; Particle Swarm Optimization

Abstract

Malfunction of the body's organs, heart failure, makes a person and those closest to them feel worried, which will result in the person's death. There are too many deaths caused by this deadly disease every year. The main problem throughout the world is heart disease, the death rate of which is getting higher and higher and is uncontrollable for various parties due to many factors, especially in terms of knowing it early or not being able to predict it accurately. Therefore, the aim of this research on heart failure problems is to improve heart failure predictions with optimal accuracy, namely the neural network method with the particle swarm optimization method. Previously, heart failure prediction research had been carried out using several methods, but here we will increase the accuracy of the methods that have been carried out. After the testing process on the neural network method and after being optimized and getting results from the particle swarm optimization method, the accuracy increased with an increase of 08.35%. As well as increasing AUC results with an increase of 0.067%. From the results of increasing the accuracy of the neural network method, testing the particle swarm optimization method on heart failure disease data can be used as a reference for stakeholders.

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References

Ahmed, A., & Hannan, S. A. (2012). Data Mining Techniques to Find Out Heart Diseases : An Overview. (Sem Qualis) International Journal of Innovative Technology and Exploring Engineering (IJITEE), 1(4), 18–23.

Alotaibi, F. S. (2019). Implementation of machine learning model to predict heart failure disease. International Journal of Advanced Computer Science and Applications, 10(6), 261–268. https://doi.org/10.14569/ijacsa.2019.0100637

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.

Austin, P. C., Tu, J. V., Ho, J. E., Levy, D., & Lee, D. S. (2013). Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes. Journal of Clinical Epidemiology, 66(4), 398–407. https://doi.org/10.1016/j.jclinepi.2012.11.008

Bumbungan, S., Kusrini, & Kusnawi. (2023). Penerapan Particle Swarm Optimization(PSO)dalam Pemilihan Parameter Secara Otomatis padaSupport Vector Machine(SVM)untuk Prediksi Kelulusan Mahasiswa Politeknik Amamapare Timika. Jurnal Teknik AMATA, 4(1), 81–93.

Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making, 20(1), 1–16. https://doi.org/10.1186/s12911-020-1023-5

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.IMPLEMENTATION

Ishaq, A., Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., & Nappi, M. (2021). Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques. IEEE Access, 9, 39707–39716. https://doi.org/10.1109/ACCESS.2021.3064084

Larose, D. T., & Larose, C. D. (2015). Data Mining And Predictive Analytics. John Wiley and Sons.

Pal, M., & Parija, S. (2021). Prediction of Heart Diseases using Random Forest. Journal of Physics: Conference Series, 1817(1). https://doi.org/10.1088/1742-6596/1817/1/012009

Priyanka, N., & Ravikumar, P. (2017). Usage of data mining techniques in predicting the heart diseases - Naïve Bayes & decision tree. Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2017. https://doi.org/10.1109/ICCPCT.2017.8074215

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

Ridwansyah, & Purwaningsih, E. (2018). Particle Swarm Optimization Untuk Meningkatkan Akurasi Prediksi Pemasaran Bank. Jurnal PILAR Nusa Mandiri, 14(1), 83–88.

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

Saqlain, M., Hussain, W., Saqib, N. A., & Khan, M. A. (2016). Identification of Heart Failure by Using Unstructured Data of Cardiac Patients. Proceedings of the International Conference on Parallel Processing Workshops, 2016-Septe, 426–431. https://doi.org/10.1109/ICPPW.2016.66

Suhardjono, Wijaya, G., & Hamid, A. (2019). PREDIKSI WAKTU KELULUSAN MAHASISWA MENGGUNAKAN SVM BERBASIS PSO. Bianglala Informatika, 7(2), 97–101.

Susanto, B. M., Hariyanto, A., & Surateno. (2018). Performance comparison of proactive and reactive routing protocol in mobile ad hoc network. J. Commun, 13(5), 218–224. https://doi.org/10.12720/jcm.13.5.218-224

Witten, I. H. (2017). DATA MINING (Fourth Edition).

Yaqin, A., Laksito, A. D., & Fatonah, S. (2021). Evaluation of Backpropagation Neural Network Models for Early Prediction of Student’s Graduation in XYZ University. International Journal on Advanced Science Engineering Information Technology, 11(2).

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How to Cite

Yuliandari, D., Yudhistira, Y., Wuryanto, A., Sidik, S., & Ayu Sariasih, F. (2024). Improving the Accuracy of Heart Failure Prediction Using the Particle Swarm Optimization Method. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(1), 210-220. https://doi.org/10.33395/sinkron.v9i1.13017