Hepatitis is an inflammation of the liver which is
one of the diseases that affects the health of millions of people
in the world of all ages. Predicting the outcome of this disease
can be said to be quite challenging, where the main challenge
for public health care services itself is due to a limited clinical
diagnosis at an early stage. So by utilizing machine learning
techniques on existing data, namely by concluding diagnostic
rules to see trends in hepatitis patient data and see what factors are affecting patients with hepatitis, can make the diagnosis process more reliable to improve their health care. The approach that can be used to carry out this prediction process is a regression technique. The regression itself provides a relationship between the independent variable and the dependent variable. By using the hepatitis disease dataset from UCI Machine Learning, this study applies a logistic regression model that provides analysis results with an accuracy rate of 83.33
