Application of the K-Nearest Neighbors (K-NN) Algorithm for Classification of Heart Failure

Authors

  • Ryan Yunus Sekolah Tinggi Teknik Pati
  • Uli Ulfa Sekolah Tinggi Teknik Pati
  • Melinna Dwi Safitri Sekolah Tinggi Teknik Pati

DOI:

https://doi.org/10.33633/jais.v6i1.4513

Abstract

Heart failure is a type of disease that has the largest number of patients in the world. Based on information from the data center, there were 229,696 people with heart failure in 2013. Lack of public knowledge about what indications of a person having heart failure make the main cause not handled properly by heart failure patients. In this study, data classification was carried out using KNN algorithm because it has a simple calculation and has a fast time. This study only uses 12 attributes, while the previous study compared 6 algorithms with 13 attributes from 299 data. The highest algorithm with 94.31% accuracy by Random Forest while KNN had an accuracy rate of 86.95% with the same data. In this study, the accuracy of the sample data was compared between 20 data and 299 total data. Both of them have different accuracy. 20 sample data has an accuracy rate of 89.29% while 299 data has an accuracy rate of 96.66%.

References

Wahyudi E, Hartati S. P. D. dan I. Kementerian Kesehatan RI, “Case-Based Reasoning untuk Diagnosis Penyakit Jantung,” IJCCS (Indonesian J. Comput. Cybern. Syst).2017; 11(1): 1.

Puspita D, Fadil M. Penggunaan Ventilasi Mekanik pada Gagal Jantung Akut. J. Kkes. Andalas. 2020; 9(1S) : 194–203.

P. D. dan I. Kementerian Kesehatan RI, “Situasi Kesehatan Jantung ; Mari Menuju Masa Muda Sehat, Hari Tua Nikmat Tanpa PTM dengan Perilaku Cerdik,” Pus. Data dan Inf., p. 8, 2014..

Tanai E, Frantz S. Pathophysiology of heart failure. Compr. Physiol. 2016; 6(1): 187–214.

Palilati N. H, Wantania FEN, and Rotty LWA. Hubungan Performa Fisik dengan Prognosis Pasien Gagal Jantung. ECL (JURNAL E-CLINIC) .2021; 9 (28): 118–123.

Putra PD and Rini DP Prediksi Penyakit Jantung dengan Algoritma Klasifikasi. Pros. Annu. Res. Semin. 2019; 5(1): 978–979.

Haryati H, Saida S, Rangki L. Kualitas Hidup Penderita Gagal Jantung Kongestif Berdasarkan Derajat Kemampuan Fisik Dan Durasi Penyakit. Faletehan Heal. J. 2020; 7(2): 70–76.

Sudoyo A, Setiyohadi B, Alwi I, Simadibrata M K, Setiati. Buku Ajar llmu Penyakit Dalam. 5th ed. 2009: .

Rahayu S, Purnama J J, Pohan A B, Nugraha FS, Nurdiani S, Hadianti S. Prediction of survival of heart failure patients using random forest. J. Pilar Nusa Mandiri. 2020; 16(2) : 255–260.

Adeniyi D A, Wei Z, Yongquan Y. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Appl. Comput. Informatics. 2016; 12(1): 90–108.

Nurjanah W E, Perdana R S, Fauzi M A. Analisis Sentimen Terhadap Tayangan Televisi Berdasarkan Opini Masyarakat pada Media Sosial Twitter menggunakan Metode K-Nearest Neighbor dan Pembobotan Jumlah Retweet. J. Pengemb. Technol. Inf. and Computer Science. Univ. Brawijaya. 2017; 1(12): 1750–1757.

Leidiyana H. Penerapan algoritma k-nearest neighbor untuk penentuan resiko kredit kepemilikan kendaraan bemotor. PIKSEL (penelitian ilmu computer, system embedded & logic) 2013; 1(1): 65–76.

Lestari M. Penerapan Algoritma Klasifikasi Nearest Neighbor (K-NN) untuk Mendeteksi Penyakit Jantung. Fact. Exacta. 2014; 7(4): 366–371.

Hidayatul S, Aini A, Sari Y A, Arwan A. Seleksi Fitur Information Gain untuk Klasifikasi Penyakit Jantung Menggunakan Kombinasi Metode K-Nearest Neighbor dan Naïve Bayes. JPTIIK (jurnal pengembangan teknologi infomarsi dan ilmu computer) 2018; 2(9): 2546–2554.

Yusra, Olivita D, Vitriani Y. Yusra, D. Olivita, and Y. Vitriani, “Perbandingan Klasifikasi Tugas Akhir Mahasiswa Jurusan Teknik Informatika Menggunakan Metode Naïve Bayes Classifier dan K-Nearest Neighbor. SiTekIn (JURNAL SAINS, TEKNOLOGI DAN INDUSTRI UIN SYARIF KASIM RIAU) 2016; 14(1): 79–85.

Wella, Made N, Iswari S. Naive Bayes dalam Pengklasifikasian Kesegaran Ikan Menggunakan Media Foto. ULTIMATICS (Jurnal Teknik Informatika) 2017; 9(2): 114–117.

Indriyanti, Sugianti D, Al Karomi M A. Peningkatan Akurasi Algoritma KNN dengan Seleksi Fitur G ain Ratio untuk Klasifikasi Penyakit Diabetes Mellitus. IC-Tech. 2017; 7(2): 1–6.

Syafi'i S I, Wahyuningrum R T, Muntasa A. Segmentasi Obyek Pada Citra Digital Menggunakan Metode Otsu Thresholding. J. Inform. 2016; 13(1): 1–8.

Ristoski P, Bizer C, Paulheim H. Mining the Web of Linked Data with Rapid Miner. J. Web Semant. 2015; 35(3): 142–151.

Prasanti A A, Fauzi M A, Furqon M T. Klasifikasi Teks Pengaduan Pada Sambat Online Menggunakan Metode N- Gram dan Neighbor Weighted K-Nearest Neighbor ( NW-KNN ). J. Pengemb. Technol. Inf. and Computer Science. Univ. Brawijaya.2018; 2(2): 594–601.

Downloads

Published

2021-05-10