Liver Diseases Prediction Using Machine Learning with Comparison Graph

Abstract

Chronic Liver Disease is the leading cause of global death that impacts the massive quantity of humans around the world. This disease is caused by an assortment of elements that harm the liver. For example, obesity, an undiagnosed hepatitis infection, alcohol misuse. Which is responsible for abnormal nerve function, coughing up or vomiting blood, kidney failure, liver failure, jaundice, liver encephalopathy and there are many more. This disease diagnosis is very costly and complicated. Therefore, the goal of this work is to evaluate the performance of different Machine Learning algorithms to reduce the high cost of chronic liver disease diagnosis by prediction. In this work, we used five algorithms Logistic Regression, K Nearest Neighbors, Decision Tree, Support Vector Machine, and Random Forest. The performance of different classification techniques was evaluated on different measurement techniques such as accuracy, precision, recall, f-1 score, and specificity. The analysis result shown the LR achieved the highest accuracy. Moreover, our present study mainly focused on the use of clinical data for liver disease prediction and explore different ways of representing such data through our analysis.