Performance Comparison of Machine Learning and Deep Learning Algorithms for Liver Disease Detection

The main cause of death worldwide, which has a significant negative influence on the vast majority of people, is liver disease. Various factors that affect the liver are the cause of this disease. For instance, alcoholism, obesity, and untreated hepatitis. The cost and complexity of this disease's diagnosis are enormous. The proposed work aims to reduce the high cost of liver disease diagnosis through detection by comparing the efficiency of machine learning (ML) and deep learning (DL) algorithms. In the proposed study, variety of algorithms have been employed that include Convolution Neural Network (CNN), Artificial Neural Network (ANN), Gaussian Naive Bayes (GNB), Random Forest (RF), and Logistic Regression (LR). The effectiveness of the performance is assessed using a variety of metrics that include accuracy, precision, recall, F-1 score, train time, and test time. Proposed work primarily focuses on the use of medical data for the detection of disease related to liver. It has been noted that the proposed work performed better than state-of-the-art work.