Liver Disease Diagnosis Using Machine Learning

Abstract

This paper evaluates the performance of various supervised machine learning algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Extra Trees, LightGBM as well as a Multilayer Perceptron (MLP) neural network in the detection and diagnosis of liver disease. Existing methods for diagnosis tend to be highly invasive and time-consuming. A lack of qualified experts exacerbates these issues. Since blood tests, known as liver function tests, are a standard method to assess liver health, these models utilize blood enzyme levels like Bilirubin, Albumin, Alanine transaminase (SGPT), and Aspartate Aminotransferase (SGOT) to diagnose liver disease in patients. A total of 11 attributes are used to train the models. The algorithms are compared using metrics including, but not limited to, F1 score, accuracy, and precision. The Extra Trees classifier is shown to provide the highest accuracy of 0.89 as well as an F1 score of 0.88. Thus, it appears to be the best method for the timely and accurate diagnosis of liver disease using blood enzyme levels. In addition, the usage of machine learning algorithms alongside human medical expertise may help drastically reduce errors in clinical diagnosis. This paper establishes the feasibility of applying machine learning in various medical fields including the diagnosis of other diseases.