Penerapan Metode Decision Tree Menggunakan Algoritma Iterative Dichotomiser 3 (ID3) Untuk Klasifikasi Resiko Penyakit Jantung

EvaEva Fauziah; Ahmad Fikri Zulfikar

Authors

EvaEva Fauziah Universitas Pamulang
Ahmad Fikri Zulfikar Universitas Pamulang

Keywords:

Heart Disease Risk, ID3 Algorithm Decision Tree, Classification

Abstract

Heart or cardiovascular disease is a condition caused by narrowing and blockage of blood vessels, which is one of the most common deadly diseases in every country. The risk of heart disease becomes an event that cannot be avoided because of a lack of attention to heart health where a healthy lifestyle and healthy eating patterns are not implemented. For this reason, an analysis of the risk of heart disease is needed. Classification is a data mining method that is widely used in determining a predictable decision based on previous data that is processed using a classification algorithm. The classification algorithm used is iterative dichotomizer 3 (ID3) using a dataset taken from the UCI Machine Learning Repository, sourced from V.A. Medical Center, Long Beach and Cleveland Clinic Foundation. The dataset consists of 14 attributes including: age, sex, cp, tresbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal, num (predictive attribute). The evaluation method used is the confusion matrix with the results of calculating an accuracy of 85.71%, a precision of 84.62% and a recal of 84.62%.

References

Aulia, W. (2018). Sistem Pakar Diagnosa Penyakit Jantung Koroner dengan Metode Probabilistic Fuzzy Decision Tree. Jurnal Sains dan Informatika, IV(2), 106-117.

Bianto, M. A., Kusrini, & Sudarmawan. (2019, Januari). Perancangan Sistem Klasifikasi Penyakit Jantung Mengunakan Naïve Bayes. Citec Journal, VI(1), 75-83.

Fatmawati. (2016, Maret). Perbandingan Algoritma Klasifikasi Data Mining Model C4.5 dan Naive Bayes untuk Prediksi Penyakit Diabetes. Jurnal Techno Nusa Mandiri, XIII(1), 50-59.

Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (Third Edition ed.).Waltham, USA: Elsevier Inc.

Islamiati, S., & Widiartha, I. M. (2015, Oktober). Klasifikasi Penyakit Jantung Menggunakan Metode Decision Tree dengan Penerapan Algoritma C5.0. Jurusan Ilmu Komputer, 308-316.

Mostafa, A. A., & Mahmoud, H. E. (2022). Review of Data Mining Concept and its Techniques. International Journal of Academic Research in Business and Social Sciences, XII(6), 611–619.

Pusporani, E., Qomariyah, S., & Irhamah. (2019). Klasifikasi Pasien Penderita Penyakit Liver dengan Pendekatan Machine Learning. Inferensi, II(1), 25-32.

Ridho, R., & Hendra. (2021, Mei). Klasifikasi Diagnosis Penyakit COVID-19 Menggunakan Metode Decision Tree. jurnal.umj.ac.id, XI(3), 69 – 75.

Senubekti, M. A., & Dewi, L. A. (2022, Juli). Prinsip Klasifikasi dan Data Mining dengan Algoritma C4.5. Jurnal Nuansa Informatika, XVI(2), 87-93.

Tyasti, A. E., Ispriyanti, D., & Hoyyi, A. (2015). Algoritma Iterative Dichotomiser 3 (ID3) untuk Mengidentifikasi Data Rekam Medis (Studi Kasus Penyakit Diabetes Mellitus Di Balai Kesehatan Kementerian Perindustrian, Jakarta). Jurnal Gaussian, IV(2), 237–246.