Comparative Analysis of Decision Tree and Artificial Neural Network Methods for Predicting Potential Heart Disease
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Abstract
Prediction models have been used in various fields such as health, education, and industry. This system can connect various data collected to be used as learning for the system in solving a problem similar to the data used as learning. The prediction model involves various elements such as mathematics, machine learning, and statistics. Heart disease remains a leading cause of mortality globally, and accurate prediction models are crucial for early detection and treatment. However, existing models often struggle with dataset imbalance, leading to suboptimal performance. This study aims to compare the performance of Decision Tree and Artificial Neural Network (ANN) models, including the Elman and Jordan variants, to identify the most suitable prediction model for heart disease with a quantitative study. The type of ANN used is multi-layer with the Elman and Jordan models. However, a comparative analysis of heart disease objects was carried out using the K-Nearest Neighbor (KNN) and Naïve Bayes methods, which resulted in Naïve Bayes being better than KNN. From all the processes that have been carried out, the researchers obtained results from precision, recall, and F1-score, which were classified as poor, with an average of 55%. The Decision Tree model achieved an average accuracy of 79%, while the Elman and Jordan networks achieved 87% and 86%, respectively. However, precision, recall, and F1-scores were relatively low, averaging 55%, likely due to dataset imbalance. The accuracy results obtained are also not always directly proportional to the amount of data used. There is a significant decline at the beginning of the process, but the accuracy obtained continues to increase until all the data is used. Apart from that, there was a spike in precision, up to 80%, in several implementation processes with prediction models. Based on the results obtained in the implementation process, it can be said that the Elman Network is superior to other methods when using accuracy benchmarks. However, the relatively low precision, recall, and F1-score results indicate the model's performance is lacking.
References
D. Larassati, A. Zaidiah, and S. Afrizal, “Sistem Prediksi Penyakit Jantung Koroner Menggunakan Metode Naive Bayes,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 7, no. 2, pp. 533–546, 2022, doi: 10.29100/jipi.v7i2.2842.
D. Saraswati, “Inovasi Pelayanan Kesehatan : Deteksi Dini Penyakit Jantung Koroner melalui Posbindu PTM,” vol. 2, pp. 10–16, 2024.
E. Haryatmi and S. Pramita Hervianti, "Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu," J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 386–392, 2021, doi: 10.29207/resti.v5i2.3007.
M. Rizq Daffa Jodi, “Fakultas Komputer Algoritma dan Struktur data,” Fak. Kompiter, vol. 1, 2020.
R. Ghorbani and R. Ghousi, "Predictive data mining approaches in medical diagnosis: A review of some diseases prediction," Int. J. Data Netw. Sci., vol. 3, no. 2, pp. 47–70, 2019, doi: 10.5267/j.ijdns.2019.1.003.
Sylvia Rheny, “Algoritma adalah: Pengertian, Fungsi, 5 Ciri, dan Contohnya,” Ekrut Media.
R. S. Wahono, Data Mining Data mining, vol. 2, no. January 2013. 2005.
S. Aulia, “Klasterisasi Pola Penjualan Pestisida Menggunakan Metode K-Means Clustering (Studi Kasus Di Toko Juanda Tani Kecamatan Hutabayu Raja),” Djtechno J. Teknol. Inf., vol. 1, no. 1, pp. 1–5, 2021, doi: 10.46576/djtechno.v1i1.964.
R. Ordila, R. Wahyuni, Y. Irawan, and M. Yulia Sari, "Penerapan Data Mining Untuk Pengelompokan Data Rekam Medis Pasien Berdasarkan Jenis Penyakit Dengan Algoritma Clustering (Studi Kasus : Poli Klinik PT.Inecda)," J. Ilmu Komput., vol. 9, no. 2, pp. 148–153, 2020, doi: 10.33060/jik/2020/vol9.iss2.181.
F. Fahrianto, "Data Warehouse dan Data Mining," GAES-PACE B. Publ., p. 193, 2016.
A. Journal et al., “Penggunaan Data Mining Sebagai Pengambilan Keputusan Penerimaan Bantuan Terhadap Rumah Ibadah (Studi Kasus : Rumah Ibadah Di Kabupaten Tanggamus),” Aisyah J. Informatics Electr. Eng., vol. 4, no. 1, pp. 55–65, 2022.
D. A. Langga and Dkk, “Perbandingan Algoritma Naive Bayes Dengan Algoritma K-Nearest Neighbor Untuk Prediksi Penyakit Jantung,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1689–1699, 2019.
I. Alhabib, “Komparasi Metode Deep Learning, Naïve Bayes Dan Random Forest Untuk Prediksi Penyakit Jantung,” INFORMATICS Educ. Prof. J. Informatics, vol. 6, no. 2, p. 176, 2022, doi: 10.51211/itbi.v6i2.1881.
D. Derisma, “Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 84–88, 2020, doi: 10.30871/jaic.v4i1.2152.
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, 2020, doi: 10.35315/dinamik.v24i2.7867.
R. Annisa, “Analisis Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Penderita Penyakit Jantung,” J. Tek. Inform. Kaputama, vol. 3, no. 1, pp. 22–28, 2019.
K. Algoritma, D. Tree, and S. V. M. Ann, “Komprasi Algoritma,” vol. 16, pp. 21–27, 2023.
Y. Y. Song and Y. Lu, "Decision tree methods: applications for classification and prediction," Shanghai Arch. Psychiatry, vol. 27, no. 2, 2015, doi: 10.11919/j.issn.1002-0829.215044.
N. V. Pusean, N. Charibaldi, and B. Santosa, "Comparison of Scenario Pre-processing Performance on Support Vector Machine and Naïve Bayes Algorithms for Sentiment Analysis," Inf. J. Ilm. Bid. Teknol. Inf. dan Komun., vol. 8, no. 1, pp. 57–63, 2023, doi: 10.25139/inform.v8i1.5667.
K. Izzah, K. Khalid, and D. Rolliawati, "Decision Support System for Determining the Feasibility of a Program Keluarga Harapan Receiver Using the Analytic Network Process Algorithm," Inf. J. Ilm. Bid. Teknol. Inf. dan Komun., vol. 5, no. 1, pp. 45–53, 2020, doi: 10.25139/inform.v5i1.2300
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