Risk Factor Prediction of Hepatitis Patient Using Machine Learning Approach
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In this study 1,032 rows of data collected from Felege Hiwot Referral Hospital, Tibebe Ghion Referral Hospital, and Addis Alem Hospital. Data were collected from patient medical records. The dataset consists of Age, Gender, Steroid, Antivirals, Fatigue, Malaise, Anorexia, Liver big, Liver firm, Spleen palpable, Spiders, Ascites, Varies, Bilirubin, Alk phosphate, Sgot, Albumin, Protime, Hemoglobin, Creatinine and Class (Low risk or High risk) attributes that are used for predicting risk factor of hepatitis patient. Six machine learning techniques, namely Decision Tree (DT), Random Forest (RF), Deep Neural Network (DNN), Extreme Grahigh risknt Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Grahigh risknt Boosting Machine (LightGBM), were combined with PSO and GA for hyperparameter optimization to identify the best-performing model.