Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees

Abstract

Early detection of liver disease is never easy, though it is one of the most important diseases on earth. This study, thus, attempts to achieve efficient early detection through a Multilayer Perceptron Neural Network (MLPNN) algorithm based on various decision tree algorithms such as See5 (C5.0), Chi square Automatic interaction detector (CHAID) and classification and regression tree (CART) with boosting technique. Five hundred and eighty-three records related to the Indian Liver Patient Dataset (ILPD) were collected from University of California, Irvine (UCI) repository dataset for the verification of the proposed work. The ILPD dataset is divided into 70% for the training stage and 30% for the testing stage. Several evaluation metrics, such as specificity, sensitivity, precision, false positive rate (FPR), false negative rate (FNR), F1, and accuracy, are applied in this study. These metrics are carried out in two phrases. In the first experiment, we observed that B-C5.0 method presents better performance than B-CHAID and B-CART methods. In the second experiment, a hybridization of B-C5.0 and MLPNN methods, namely MLPNNB-C5.0, indicates the highest rates of detection of liver disease compared to other algorithms. Results indicate that MLPNNB-CHAID method has the most innovative accuracy with a value of 14.57%. The proposed method is able to diagnose and classify the liver disease efficiently. We would argue that the proposed system can be useful as a medical data mining approach in order to provide an early diagnosis of liver disease.