A Comparative Analysis with Various Machine Learning Algorithms for Prediction of Liver Cirrhosis

This paper represents the comparative analysis for prediction of liver cirrhosis using various machine algorithms. In recent years, the liver diseases are becoming so fatal that more investigations are required to correctly diagnose the disease profile. In this regard, the machine learning algorithms provide a good platform for forecasting the liver cirrhosis diseases in the people coming hospitals with various symptoms. In this paper, confusion matrix, accuracy score, F1 score have been used as a decision tool for various machine learning algorithms including SVM(Support vector machine), Logistic regression, Random forest, KNN(K Nearest Neighbor), Gaussian Naïve Bayes. After smote re sampling the output feature, it has been observed that the Random forest has successfully predicted the liver cirrhosis with a F1 score of 0.82.