Risk Factor Prediction of Hepatitis Patient Using Machine Learning Approach
Risk Factor Prediction of Hepatitis Patient Using Machine Learning Approach
| Accessed Date | 2024-12-24T13:27:33Z | |
| Date Availabe | 2024-12-24T13:27:33Z | |
| Issued Date | 2024 | |
| Description | 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. | |
| URI | https://rdmc.aphi.gov.et/handle/123456789/110 | |
| Language | en | |
| Title | Risk Factor Prediction of Hepatitis Patient Using Machine Learning Approach | |
| Type | Dataset | |
| Entity Type | ||
| Geographic Coverage | Facility | |
| Sex Coverage | Yes | |
| Data Quality | Good | |
| Format | Excel | |
| RDMC ID | APHI-RDMC-057 | |
| Keyword | Hepatitis, Risk factor prediction, machine learning, Model Explainability | |
| Objective | To develop a machine learning model for predicting the risk factor of hepatitis patients and elucidate the predictive mechanisms of the models | |
| Study Population | All hepatitis patients attending during the data collection period was the study population. |
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