Boosting Ensemble Model for Prediction Hepatitis C (BEM-HC)

ABSTRACT

Hepatitis is a viral infection that affects the liver. It may cause several illnesses associated with the liver. The upsurge in hepatitis cases base on global statistics shows the essence of an enhanced model for the reliable diagnosis of hepatitis. This study relying on the efforts of previous predictive models has enacted ensemble machine learning technique-specifically boosting. The Boosting Ensemble Model for predicting Hepatitis C (BEM-HC) was initiated with Support Vector Machine (SVM) and Decision Tree (DT) as weaker learners. The BEM-HC was subsequently implemented with validation results captured as 68% accuracy while obtaining a 57% Root Means Square Error (RMSE).

Keyboard: Decision Tree (DT), Support Vector Machine (SVM), Hepatitis C, Prediction