Prediction of Hepatitis Disease Using Machine Learning Algorithm

Hepatitis is currently one of the worst diseases that kill people all around the world. The human liver's inflammation is brought on by it. If people are successful in identifying this dangerous condition early on, can prevent many individuals from dying from it. This project review a variety of data mining methods is used to predict the hepatitis disease[1]. In addition, project offered a respectable method for enhancing the effectiveness of prediction models. For the hepatitis disease sample dataset, different classification algorithms are used to calculate prediction accuracy. F1-score, precision, recall, accuracy, and ROC are calculated to compare the performance of categorization models. The algorithm which gives the highest accuracy will be the best classification algorithm

Keywords

Data Mining, KNN, Naive Bayes, SVM, MLP, Random Forest

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