Jurnal Penerapan Decision Tree untuk Klasifikasi Penyakit Berdasarkan Data Rekam Medis
Penerapan Decision Tree untuk Klasifikasi Penyakit Berdasarkan Data Rekam Medis
Abstract
This research discusses the application of the Decision Tree algorithm for disease classification based on medical record data. The patients' medical records are analyzed using this algorithm to identify significant patterns in disease diagnosis. The resulting model is evaluated using metrics such as accuracy, precision, and recall, showing adequate results with an accuracy of 91.55%. Visualization in the form of a decision tree highlights the key attributes influencing classification, such as chest pain type, cholesterol level, and patient age. This study emphasizes the effectiveness of the Decision Tree algorithm in supporting data-driven medical decision support systems, although careful data processing is required to address the challenges of medical record data quality.
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References
[2] D. K. Destiani et al., “Klasifikasi Sinyal Ecgagal Jantung Menggunakan Wavelet Dan Jst Propagasi Balik Dengan Modifikasi Gradien Konjugat Polak-Ribiere Heart Failure Ecg Signal Classification Using Wavelet and Ann Backpropagation With Polak-Ribiere Conjugate Gradient,” vol. 5, no. 1, pp. 1811–1818, 2018.
[3] J. Informatika et al., “Penerapan Algoritma Naïve Bayes Untuk Mengetahui Pasien Penyakit Gagal Jantung Jurnal Informatika Dan Rekayasa Komputer ( JAKAKOM ),” vol. 2, no. September, 2022.
[4] Y. Yuliani, “Algoritma Random Forest Untuk Prediksi Kelangsungan Hidup Pasien Gagal Jantung Menggunakan Seleksi Fitur Bestfirst,” Infotek J. Inform. dan Teknol., vol. 5, no. 2, pp. 298–306, 2022, doi: 10.29408/jit.v5i2.5896.
[5] W. S. Dharmawan, “I N F O R M a T I K a Dalam Prediksi Penyakit Jantung,” J. Inform. Manaj. dan Komput., vol. 13, no. 2, pp. 31–41, 2021.
[6] E. Nurlia and U. Enri, “Penerapan Fitur Seleksi Forward Selection Untuk Menentukan Kematian Akibat Gagal Jantung Menggunakan Algoritma C4.5,” J. Tek. Inform. Musirawas) Elin Nurlia, vol. 6, no. 1, p. 42, 2021.
[7] H. M. Nawawi, J. J. Purnama, and A. B. Hikmah, “Komparasi Algoritma Neural Network Dan Naïve Bayes Untuk Memprediksi Penyakit Jantung,” J. Pilar Nusa Mandiri, vol. 15, no. 2, pp. 189–194, 2019, doi: 10.33480/pilar.v15i2.669.
[8] D. A. M. Reza, A. M. Siregar, and Rahmat, “Penerapan Algoritma K-Nearest Neighbord Untuk Prediksi Kematian Akibat Penyakit Gagal Jantung,” Sci. Student J. Information, Technol. Sci. , vol. III, no. 1, pp. 105–112, 2022.
[9] R. Wajhillah, “Particle Swarm Optimization Untuk Prediksi Penyakit Jantung,” vol. I, no. 1, pp. 26–36, 2014.
[10] D. P. Utomo and M. Mesran, “Analisis Komparasi Metode Klasifikasi Data Mining dan Reduksi Atribut Pada Data Set Penyakit Jantung,” J. Media Inform. Budidarma, vol. 4, no. 2, p. 437, 2020, doi: 10.30865/mib.v4i2.2080.
[11] Oskar, Fransiska Ria, Rupina, and N. P, “Pengelolaan Data Penyakit Jantung Dengan Menggunakan Metode Naive Bayes,” J. Sains Dan Komput., vol. 8, no. 02, pp. 49–54, 2024, doi: 10.61179/jurnalinfact.v8i02.531.
[12] M. Metode, K. N. Dan, and L. Regression, “Implementasi data mining untuk memprediksi penyakit jantung menggunakan metode k-nearest neighbor dan logistic regression,” vol. 5, pp. 493–501, 2022, doi: 10.37600/tekinkom.v5i2.698.
[13] R. Fadnavis, K. Dhore, D. Gupta, J. Waghmare, and D. Kosankar, “Heart disease prediction using data mining,” J. Phys. Conf. Ser., vol. 1913, no. 1, 2021, doi: 10.1088/1742-6596/1913/1/012099.
[14] M. C. Arta, N. Anwar, Y. A. Putri, and M. Asroll, “Implementasi Prediksi Penyakit Jantung Menggunakan Data Mining Untuk Dunia Kesehatan Pre-processing,” vol. 10, no. 01, pp. 42–48, 2024.
[15] I. S. B. Azhar and W. K. Sari,