Multi Perceptron Neural Network and Voting Classifier for Liver Disease Dataset

The liver is one of the most significant organs in the human body. We can predict liver disease in a patient at an early stage based on previously predicted values using data from patients with abnormal liver function. Which helps the doctors to make a diagnosis. In this paper, the liver function test is analyzed for predicting liver disease, where the input of the patient’s details and output data are passed into various classifiers such as Support Vector Machine, K-Nearest Neighbor, Hard Voting Classifier, and Deep Neural Network Multilayer Perceptron Techniques. Model Evaluation Criteria such as the Confusion Matrix, Precision Score, Recall, Accuracy, Specificity, and F-score are used to determine the best model. A dataset of 583 individuals suffering from liver disease is analyzed and we found that Hard Voting Classifier (HVC) is the best for this dataset. Additionally, this Voter Classifier prediction algorithm gives higher accuracy, which will help to diagnose liver disease.