Multi Perceptron Neural Network and Voting Classifier for Liver Disease Dataset

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