Liver Disease Prediction using Multi-Layer Perceptron (MLP) Deep Learning Technique

Abstract

As liver diseases are the causes of many health issues in the world, there is a need for accurate and efficient diagnostic methods to detect liver diseases as early as possible. In this study, a model for the classification of liver diseases using Multi-Layer Perceptron (MLP) Neural Network is developed, which is good at capturing complex and non-linear relationships in data sets. The training and testing used data comprising information on bilirubin, enzymes, and patient characteristics for liver disease. Normalization, feature selection, and discarding outliers are used to increase the accuracy of the model and avoid it becoming too fitting for the training data. It has several hidden layers with ReLU activation, and Adam is used to optimize its parameters. Common ways to assess the model are by looking at its accuracy, precision, recall, F1-score, and ROC-AUC. The experimental results reveal that the proposed MLP model has an overall accuracy of 92.26% while the accuracy of conventional machine learning algorithms such as decision tree, support vector machine and KNN are 78.57%, 83.47% and 76.32%, respectively. Moreover, the model exhibits good generalization performance on various subsets of data and is therefore relevant for clinical use. Doctors can benefit from the use of MLPs to make faster and more accurate diagnosis of liver diseases, thereby improving patients' health outcomes. Future work aims at connecting the model to real-time clinical decision aids for clinical management and further developing the framework to enable the detection of specific liver disease modalities as Hepatitis, Cirrhosis, fatty liver disease etc.