Algorithm 4 Extra Tree Classification to Predict Liver Disease

Input:     Training set record Output:   Class of record (liver disease or no liver disease) Generating Algorithm Begin      Step 1: Randomly split data into subsets equal to the number of classifiers say n with random feature selection and random-split      Step 2: Train n subsets on n decision trees, respectively      Step 3: Testing          Step 3.1: Calculate the output of the test record on each base learner          Step 3.2: Calculate the final predicted value by using the voting method End