Cirrhosis is a liver disease caused by long-term liver damage. Scar tissue caused by cirrhosis prevents the liver from working properly. With the hepatitis C virus, 130-150 million people are infected in the world and 350-500 thousand deaths, and 3-4 million new cases are reported every year due to liver disease. In 2030, it is predicted that there will be 40 percent increase in compensated cirrhosis due to the hepatitis C virus, 60 percent increase in decompensated cirrhosis, and 70 percent increase in liver-related deaths. Although it is difficult to diagnose cirrhosis in the early stages, it is very important step for its treatment. Blood tests, imaging tests, and biopsy methods are used to detect cirrhosis. Due to the costs of these tests and the inability to get the test results immediately, the treatment of the patients cannot be started immediately. In this study, a MLP-based deep learning model has been developed for the prediction of cirrhosis. The developed model has been compared with DT, kNN, LR, NB, RF, and SVM. Experimental studies using the accuracy, precision, recall, and F1-score showed that the developed model was more successful than the compared models. Experimental results showed that the developed model had 80.48% accuracy, 85.71% precision, 85.71% recall, and 85.71% F1-score. Experimental results showed that the developed model had a prediction accuracy of over 80% and F1-score of over 85% in cirrhosis detection from blood tests. The developed model can be used in real-world applications to alleviate the workload of healthcare professionals and to develop early diagnosis systems.