Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner

Authors

  • Nurhadi Surojudin Universitas Pelita Bangsa, Indonesia
  • Ermanto Ermanto Universitas Pelita Bangsa, Indonesia
  • Muhtajuddin Danny Universitas Pelita Bangsa, Indonesia
  • Suria Pratama Universitas Pelita Bangsa, Indonesia

DOI:

https://doi.org/10.47709/brilliance.v4i1.4136

Keywords:

Heart Failure, Naive Bayes Algorithm, RapidMiner

Abstract

Until now there is no treatment that can specifically treat heart failure problems. Heart failure treatment only functions to control symptoms, improve quality of life so that patients can carry out normal activities, and reduce the risk of complications due to heart failure such as heart rhythm disturbances, kidney and lung function disorders, stroke, and sudden death. Heart failure is a condition when the heart pump weakens so that it is unable to circulate sufficient blood throughout the body. This condition is also called congestive heart failure. Until now there is no treatment that can specifically treat heart failure problems. This research is a descriptive study which aims to describe the condition of heart failure. By using classification techniques in data mining on data from patients suffering from heart failure using the Naive Bayes algorithm. By using the Rapid Miner tool, data processing is based on the dataset, using classification techniques and data mining stages to classify data on patients suffering from heart failure. By using the Rapid Miner tool, the data processing that will be used as a data collection in this research is collected into 90% training data and 10% testing data. The research results showed an accuracy rate of 80.00%, precision of 66.67% and recall of 100.00%. Based on the research that has been conducted, it is concluded that classification techniques using the Naive Bayes algorithm can be used to determine the potential for life and death in heart failure sufferers.

References

Andy Kristiyan, Ninuk Dian Kurniawati, & Junait Junait. (2023). Aplikasi Edukasi Presisi Manajemen Cairan terhadap Kemampuan Manajemen Cairan pada Pasien Congestive Hearth Failure (CHF). Jurnal Keperawatan, 16(1), 383–396.

Andy Susbandiyah Ifada, Deswati Ilahillaili Sarkiyah, & Rizki Nugrahani. (2017). Kepatuhan Terapi Farmakologi dan Non Farmakologi Pada Pasien Diabetes Mellitus Tipe II di Puskesmas Tanjung Karang Tahun 2017. Jurnal Ilmu Kesehatan Dan Farmasi, 5(2), 50–53.

Dewi, Y. N., & Sariasih, F. A. (2019). METODE SAMPLE BOOTSTRAPPING UNTUK MENINGKATKAN PERFORMA ALGORITMA NAIVE BAYES PADA CITRA TUNGGAL PAP SMEAR. JURNAL TEKNIK INFORMATIKA, 12(1), 1–10. https://doi.org/10.15408/jti.v12i1.11031

Erdania, E., Faizal, M., & Anggraini, R. B. (2023). FAKTOR – FAKTOR YANG BERHUBUNGAN DENGAN KEJADIAN PENYAKIT JANTUNG KORONER (PJK) Di RSUD Dr. (H.C.) Ir. SOEKARNO PROVINSI BANGKA BELITUNG TAHUN 2022. Jurnal Keperawatan, 12(1), 17–25. https://doi.org/10.47560/kep.v12i1.472

Felsi Ratna Sari, Anik Inayati, & Nia Risa Dewi. (2023). Penerapan Hand-Held Fan Terhadap Dyspnea Pasien Gagal Jantung Di Ruang Jantung Rsud Jend. Ahmad Yani Kota Metro). Jurnal Cendekia Muda, 3(3), 323–330. https://jurnal.akperdharmawacana.ac.id/index.php/JWC/article/view/475

Latifardani, R., & Hudiyawati, D. (2023). Fatigue Berhubungan dengan Kualitas Hidup pada Pasien Gagal Jantung. Jurnal Keperawatan Silampari, 6(2), 1756–1766. https://doi.org/10.31539/jks.v6i2.5697

Massie, J. I., & Widodo, S. M. S. P. A. M. (2023). Deep Learning untuk Klasifikasi Penyakit Retinopati Diabetik Menggunakan Arsitektur Alexnet dan Generative Adversarial Network. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 14(2), 251–260. https://doi.org/10.24176/simet.v14i2.9498

Muhammad Faiz Fahrizal Ardhiansyah, & Dian Hudiyawati. (2023). Hubungan Tingkat Stres Dengan Kualitas Tidur Pada Pasien Gagal Jantung. Health Information Jurnal Penelitian, 15.

Nursita, H., & Pratiwi, A. (2020). Peningkatan Kualitas Hidup pada Pasien Gagal Jantung: A Narrative Review Article (Improved Quality of Life in Heart Failure Patients: A Narrative Review Article). Jurnal Berita Ilmu Keperawatan, 13(1), 10–21. https://doi.org/10.23917/bik.v13i1.11916

Saida, S., Haryati, H., & Rangki, L. (2020). Kualitas Hidup Penderita Gagal Jantung Kongestif Berdasarkan Derajat Kemampuan Fisik dan Durasi Penyakit. Faletehan Health Journal, 7(02), 70–76. https://doi.org/10.33746/fhj.v7i02.134

Selva Dwi Prahasti, & Lukman Fauzi. (2021). Risiko Kematian Pasien Gagal Jantung Kongestif (GJK): Studi Kohort Retrospektif Berbasis Rumah Sakit. Indonesian Journal of Public Health and Nutrition, 1(3), 388–395.

Siallagan, A. M. (2021). SYSTEMATIC REVIEW: KUALITAS HIDUP PASIEN GAGAL JANTUNG KONGESTIF. Jurnal Medika?: Karya Ilmiah Kesehatan, 6(2). https://doi.org/10.35728/jmkik.v6i2.696

Winda Sinthya Naomi, Intje Picauly, & Sarci Magdalena Toy. (2021). FAKTOR RISIKO KEJADIAN PENYAKIT JANTUNG KORONER (Studi Kasus di RSUD Prof. Dr. W. Z. Johannes Kupang). Media Kesehatan Masyarakat, 3(1), 99–107. https://ejurnal.undana.ac.id/index.php/MKM/article/view/3622

Yunisa Arini Putri, Fakhira Arminda, & RE Rizal Effendi. (2023). Penatalaksanaan Gagal Jantung Kongestif pada Pria Usia 73 Tahun dengan Prinsip Pendekatan Kedokteran Keluarga. Jurnal Penelitian Perawat Profesional, 5(1), 323–334.

Yunita, Y. (2021). Implementasi K-Nearest Neighbor Dalam Prediksi Mahasiswa Berhenti Kuliah. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(3), 866. https://doi.org/10.30865/mib.v5i3.3049

Downloads

Published

2024-07-08

How to Cite

Surojudin, N., Ermanto, E., Danny, M., & Pratama, S. (2024). Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner. Brilliance: Research of Artificial Intelligence, 4(1), 294–302. https://doi.org/10.47709/brilliance.v4i1.4136

Most read articles by the same author(s)

Similar Articles

1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.