Liver Disease Prediction Using an Ensemble Based Approach

Lately, the usage of Information system and strategic tools in the domain of medical science is constantly growing. The liver is an Exocrine Gland that helps in the digestion process, especially for fats along with altering the pH of food due to its alkaline nature. One of the most common signs for the majority of liver disease is hyperbilirubinemia which is very difficult to identify at an early stage. While certain diseases such as obstructive Jaundice and acute viral hepatitis present themselves with an early rise of bilirubin along with yellowish discoloration of the skin, many other diseases don’t usually present themselves with an early rise of serum bilirubin or skin discoloration because of which sometimes liver disease are overlooked or misdiagnosed in primary level. However, serum bilirubin is not the only way to diagnose liver disease because it is not specific. The most specific way to diagnose liver disease is by liver function test. With the help of the detection of the enzyme level, we can identify and confirm the presence of liver disease and intensity of liver damage when coupled with suitable imaging modalities like Ultrasound, CT scan, or MRI scan. The dataset contains patient parameters such as Age, Sex, Total Proteins, Alkaline Phosphatase, Alanine Phosphatase, Total Proteins, Total Bilirubin, Albumin, Albumin and Globulin Ratio, Direct Bilirubin, and the Result. We are using Binary Classification which is basically classifying the element of given set into two given sets i.e., Patient suffering from Liver disease or not. We will try to use an Ensemble Based Approach to find the best prediction accuracy.