Identifikasi Penyakit Jantung Menggunakan Machine Learning: Studi Komparatif
DOI:
https://doi.org/10.26623/transformatika.v21i2.7144Keywords:
Penyakit jantung, machine learning, studi komparatifAbstract
Heart disease is the number one cause of death globally. This condition is followed by an unhealthy lifestyle. Heart disease prediction needs to be done considering the importance of health. The presence of machine learning has made it easier for humans to make early detection of patterns approaching heart disease. This study compares 6 machine learning methods for disease classification with KNN, Naïve Bayes, Decision tree, Random forest, logistic regression, and SVM. The final classification obtained ranking accuracy with the highest value in the KNN method with precision, accuracy, re-call, fi-score tests. It is hoped that these results can be applied to real case studies of heart disease.
References
WHO, “WHO,” 2023. https://www.who.int/health-topics/cardiovascular-diseases (accessed Jan. 16, 2023).
Kemenkes, “kemenkes,” Sehat Negeriku, Sep. 28, 2022. https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20220929/0541166/penyakit-jantung-penyebab-utama-kematian-kemenkes-perkuat-layanan-primer/ (accessed Jan. 16, 2023).
“CVD WHO,” 2023. https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) (accessed Jan. 16, 2023).
N. Musyaffa and B. Rifai, “MODEL SUPPORT VECTOR MACHINE BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK PREDIKSI PENYAKIT LIVER,” vol. 3, no. 2, 2018.
M. Bari, S. H. Sitorus, and U. Ristian, “IMPLEMENTASI METODE NAÏVE BAYES PADA APLIKASI PREDIKSI PENYEBARAN WABAH PENYAKIT ISPA (Studi Kasus: Wilayah Kota Pontianak),” vol. 06, no. 03, 2018.
S. Sulastri, K. Hadiono, and M. T. Anwar, “ANALISIS PERBANDINGAN KLASIFIKASI PREDIKSI PENYAKIT HEPATITIS DENGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR, NAÏVE BAYES DAN NEURAL NETWORK,” Dinamik, vol. 24, no. 2, pp. 82–91, May 2020, doi: 10.35315/dinamik.v24i2.7867.
E. C. P. Witjaksana, R. Saedudin, and V. P. Widartha, “PERBANDINGAN AKURASI ALGORITMA RANDOM FOREST DAN ALGORITMA ARTIFICIAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DIABETES”.
R. Ridho, “KLASIFIKASI DIAGNOSIS PENYAKIT COVID-19 MENGGUNAKAN METODE DECISION TREE”.
maskuri, “Penerapan Algoritma K-Nearest Neighbor (KNN) untuk Memprediksi Penyakit Stroke,” J. Ilm. Intech Inf. Technol. J. UMUS, 2022.
V. Angkasa and J. J. Pangaribuan, “KOMPARASI TINGKAT AKURASI RANDOM FOREST DAN KNN UNTUK MENDIAGNOSIS PENYAKIT KANKER PAYUDARA,” Inf. Syst. Dev., vol. 7, 2022.
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