Comparative Analysis of Machine Learning Algorithms with Sequential Feature Selection and Gridsearch Optimization

(Hepatitis Case Study)

Abstract

Hepatitis is a dangerous disease that can cause liver damage. It is often difficult to diagnose because the symptoms of different categories of hepatitis are hard to distinguish. Hepatitis that is not promptly and properly managed, especially in individuals with chronic liver conditions, can lead to complications. This study aims to improve model performance in hepatitis diagnosis using medical data from hepatitis A and B patients at Citra Husada Hospital in Jember. The results show that the method of gridsearch tuning with SMOTE and SFS is very effective in enhancing the performance of the random forest and extra tree algorithms in managing hepatitis symptom data. The best performance was achieved by the random forest algorithm with an 80:20 data ratio, reaching the highest accuracy of 94.87%, recall of 95.96%, precision of 93.33%, f1-score of 94.07%, specificity of 97.97%, and ROC AUC of 99.13%