Liver Disease Diagnosis Using Machine Learning Algorithm

Liver disease is one of the most terrifying diseases. This disease is caused by a combination of factors that harm the liver. Obesity, an undiagnosed hepatitis infection, alcohol abuse, which causes abnormal nerve function, coughing up or vomiting blood, kidney failure, liver failure, jaundice, liver encephalopathy, and many other conditions are examples. Early detection of a liver infection is critical for effective treatment. Because of the subtle symptoms, medical researchers face a difficult task in predicting the disease in its early stages. Symptoms frequently appear when it is too late. To address this issue, this project will use machine learning approaches to improve liver disease diagnosis. The primary goal of this study is to use a classification algorithm to distinguish between liver patients and healthy people. Based on chemical compounds (bilirubin, albumin, proteins, alkaline phosphatase) found in the human body and tests such as SGOT and SGPT, the outcome indicates whether a person is a patient who requires diagnosis or not. Patients with liver disease have been steadily increasing as a result of excessive alcohol consumption, inhalation of harmful gases, and consumption of contaminated food, pickles, and drugs. This project’s goal is to analyze prediction algorithms in order to reduce doctors’ workload.