Data Mining Model for Designing Diagnostic Applications Inflammatory Liver Disease

Lihat/Buka File Repository

Lihat/Buka File Peer Review




The liver is a vital organ for humans. Liver disease is a disorder of every liver function. Early diagnosis of  liver disease  is very important so that it can be treated and treated quickly.  In the medical field, diagnosing inflammatory liver disease has become a rather difficult thing to do. However, there are medical records that store the patient's symptoms. This is certainly very beneficial for medical personnel or doctors. They can use previous medical records as material for making decisions about the patient's disease diagnosis. The conventional manual analysis technique that has been used so far is no longer effective for diagnosis. Along with the development of medical knowledge-based systems, the demand for the use of computer-based knowledge systems as an analytical technique in diagnosing diseases is becoming increasingly important. In this study, researchers will apply and compare several data mining classification methods, including the C4.5 algorithm, Naïve Bayes, and k-Nearest Neighbor to diagnose inflammatory  liver disease, then compare which of the three methods is the most accurate. Based on the results of measuring the performance of the three models using the Cross Validation, Confusion Matrix and ROC Curve methods, it is known that the C4.5 method is the best method with an accuracy of 70.99% and an under the curva (AUC) value of 0.950, then the k-Nearest Neighbor method with accuracy of 67.19% and the value under the curve (AUC) 0.873, then the naïve Bayes method with an accuracy rate of 66.14% and a value under the curve (AUC) of 0.742


Bidang ilmu
Data Mining


Amrin, A. (2018a). Aplikasi Diagnosa Penyakit Tuberculosis Menggunakan Algoritma Data Mining.  Jurnal Paradigma, XX(2), 91–97. 

Amrin, A. (2018b). Aplikasi Diagnosa Penyakit Tuberculosis Menggunakan Algoritma Naive Bayes.  Jurikom, 5(5), 498–502. 

Gorunescu, F. (2011). Data Mining: Concepts, Models, and Techniques. Springer. 

Handrianto, Y., & Farhan, M. (2019). C.45 Algorithm for Classification of Causes of Landslides. SinkrOn, 4(1), 120–127. 

Hannan, A., Manza, R., & Remteke, R. (2010). Generalized Regression Neural Network and Radial Basis Function for Heart Disease diagnosis. International Journal of Computer Application (0975-8887), 7(13), 7–13. 

Kusrini, & Luthfi, E. . (2009). Algoritma Data Mining. Andi Publishing. 

Nahar, N., & Ara, F. (2018). Liver Disease Prediction by Using Different Decision Tree Techniques. International Journal of Data Mining & Knowledge Management Process,  8(2), 01–09. 

Neshat, M., & Yaghoobi, M. (2009). Designing a Fuzzy Expert System of Diagnosing the Hepatitis B Intensity Rate and Comparing it with Adaptive Neural Network Fuzzy System. Proceeding of the World Congress on Engineering and Computer Science 2009,Vol II, WCECS 2009, ISBN:978-988-18210-2-7, 1–6. 

Pahlevi, O., Sugandi, A., & Sintawati, I. D. (2018). Penerapan Algoritma Apriori Dalam Pengendalian Kualitas Produk. SinkrOn, 3(1), 272–278. 

Prayoga, N. D. (2018). Sistem Diagnosis Penyakit Hati Menggunakan Metode Naïve Bayes.  Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(8), 2666–2671. 

Pusporani, E., Qomariyah, S., & Irhamah. (2019). Klasifikasi Pasien Penderita Penyakit Liver dengan Pendekatan Machine Learning. Inferensi, 2(1), 25–32. 

Setiawati, I., Wibowo, A. P., & Hermawan, A. (2019). Implementasi Decision Tree Untuk Mendiagnosis Penyakit Liver. JOISM : Jurnal of Information System Management, 1(1), 13–17. 

Sumpena, Akbar, Y., Nirat, & Henky, M. (2019). Comparison of C4 . 5 Algorithm and Naïve Bayes for Last Information on ICU Patients. SinkrOn, 4(1), 88–94. 

Thirunavukkarasu, K., Singh, A. S., Irfan, M., & Chowdhury, A. (2018). Prediction of liver disease using classification Algorithms.  2018 4th International Conference on Computing Communication and Automation, ICCCA 2018, 1(1), 1–3. 

Vercellis, C. (2009). Business Intelligent: Data Mining and Optimization for Decision Making. John Willey & Sons, Ltd. 

Wu, X., & Kumar, V. (2009). The Top Ten Algorithms in Data Mining. CRC Press.