Data Mining Techniques for Optimization of Liver Disease Classification

Patients with liver disease continue to increase and the symptoms of the disease is difficult to detect. Therefore many people suffer from liver damage but they feel healthy, it causes many medical practitioners to often fail to detect the disease. Failure to detect can mislead to improper medication and medical treatment. Therefore accurate detection is necessary to help the medical practitioner to give proper medication and medical treatment. Some researchers have been using data mining techniques to classify liver disease. The problem is, it is not easy to have the same consensus for a better algorithm in classifying liver disease. This study aims to identify if the patients have the liver disease based on the 10 important attributes of liver disease using a Decision Tree, Naive Bayes, and NBTree algorithms. The result shows NBTree algorithm has the highest accuracy, however the Naive Bayes algorithm gives the fastest computation time. This study presents promising results in giving recommendation if the patients have the disease.