| dc.description.abstract | Chronic liver disease includes chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. Also, liver disease is a severe disease, especially in Asia. In this study, we consider three binary classifications which are hepatocellular carcinoma(HCC), liver cirrhosis(LC), and fatty liver disease(FLD). All of these patients are compared with people without these diseases. There are two types of data in our data set. One is clinical or laboratory numerical data, and the other is sublingual vein imaging. We use feature selection techniques to select salient features from numerical data. Furthermore, we utilize principal component analysis (PCA) and convolutional neural network (CNN) for feature extraction from imaging. Following this, we combine these two sources of information and employ six machine learning algorithms, including Random Forest(RF), Support Vector Machines(SVM), K-Nearest Neighbors(KNN), Ridge, Logistic Regression(LR), and Multilayer Perceptron(MLP) for training.The results show that there is higher accuracy for classifying FLD(80.7%) than HCC(74.0%) and LC(64.5%) by utilizing numerical data. In addition, imaging has accuracy for classifying HCC and LC, with results as statistically significant(HCC: 52.4%, LC: 55.1%). In the case of HCC and LC, the accuracy is lower by utilizing imaging than by utilizing numerical data. Finally, we combine two data types and employ feature extraction, feature selection, and grid search in our model. The mean accuracy for HCC, LC, and FLD is 77.8%, 70.52%, and 82.6%, respectively. We improve the mean of the accuracy by 2~6% by combining features. Moreover, the best accuracy is 81.8% for HCC through MLP, CNN, and backward elimination; the best accuracy is 73.4% for LC through MLP, CNN, and RidgeCV; the best accuracy is 85.6% for FLD through RF, CNN, and backward elimination. In conclusion, in addition to blood tests and ultrasound examinations to detect liver disease, the proposed method can be potentially used as a second opinion to overcome the limitations of classical diagnostic approaches. | en_US |