Risk Association of Liver Cancer and Hepatitis B with Tree Ensemble and Lifestyle Features

• Published in: International journal of environmental research and public health Vol. 19; no. 22; p. 15171
• Main Authors: Koh, Eunji, Kim, Younghoon
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.11.2022; MDPI
• Subjects: Accuracy; Datasets; Decision trees; Hepatitis; Hepatitis B; Hepatitis B - complications; Hepatitis B - epidemiology; Humans; Life Style; Lifestyles; Liver; Liver cancer; Liver Cirrhosis; Liver diseases; Liver Neoplasms - epidemiology; Liver Neoplasms - etiology; Machine learning; Medical diagnosis; Morbidity; Nutrition; Nutrition Surveys; Patients; Performance evaluation; Regression analysis; Risk; Sensitivity analysis; Support vector machines; liver cancer; random forest; hepatitis B; lifestyle features; public health data; risk association model
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph192215171

The second-largest cause of death by cancer in Korea is liver cancer, which leads to acute morbidity and mortality. Hepatitis B is the most common cause of liver cancer. About 70% of liver cancer patients suffer from hepatitis B. Early risk association of liver cancer and hepatitis B can help prevent fatal conditions. We propose a risk association method for liver cancer and hepatitis B with only lifestyle features. The diagnostic features were excluded to reduce the cost of gathering medical data. The data source is the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2019. We use 3872 and 4640 subjects for liver cancer and hepatitis B model, respectively. Random forest is employed to determine functional relationships between liver diseases and lifestyle features. The performance of our proposed method was compared with six machine learning methods. The results showed the proposed method outperformed the other methods in the area under the receiver operator characteristic curve of 0.8367. The promising results confirm the superior performance of the proposed method and show that the proposed method with only lifestyle features provides significant advantages, potentially reducing the cost of detecting patients who require liver health care in advance.