Data Mining with Logistic Regression and Support Vector Machine for Hepatitis Disease Diagnosis - Amrin, Amrin; Rudianto, Rudianto; Sismadi , Sismadi (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2025)
Hepatitis is a chronic and dangerous disease that can lead to death. Making early predictions to detect hepatitis is very important because many people still underestimate the disease. These predictions can be made by collecting patient data or health examination results, so that preventive measures can be taken faster and better. Early diagnosis of the disease is important for prompt management and treatment. The right stage of diagnosis activities and accurate disease prediction in time can save many patients. The magnitude of this disease problem in Indonesia can be known from various studies, studies, and disease observation activities. In this study, researchers will apply and compare data mining classification methods, namely the Logistic Regression method and Support Vector Machine to diagnose hepatitis disease. Based on the research, it is known that the Logistic Regression method has an accuracy rate of 84.62% and an under the curve (AUC) value of 0.841, then the Support Vector Machine method has an accuracy rate of 87% and an AUC value of 0.865. From the t-test results, it can be seen that there is no significant difference between the Logistic Regression and Support Vector Machine methods, because the value = 0.520>0.05. This shows that the Logistic Regression method has almost the same performance as the Support Vector Machine method. Hopefully the results of this research can help doctors determine a diagnosis more quickly and reduce the possibility of misdiagnosis so that early detection of hepatitis can be carried out more widely, especially in remote areas with limited health facilities