Hepatitis Prediction Using K-NN, Naive Bayes, Support Vector Machine, Multilayer Perceptron and Random Forest, Gradient Boosting, K-Means

Hepatitis is a serious disease that causes death throughout the world. It is responsible for inflammation in the human liver. If we manage to detect this life-threatening disease early, we can save many lives from it. In this research paper, we predict hepatitis disease using data mining techniques. We have attempted to propose a feasible approach to improve the performance of our prediction models in our research. We address the problem of missing values in the dataset by replacing them with the mean value. Nine algorithms were applied to the hepatitis disease dataset to calculate prediction accuracy. We measure accuracy, precision, recall, ROC and best score, and we compare them with random search hyperparameter tuning. It is hoped that by using them we will find the optimal combination of hyperparameters to improve the performance of machine learning models which helps us compare the performance of classification models.