Cirrhosis Disease Classification by using Polynomial Feature and XGBoosting

Every year, over 2.8% of adult fatalities in India are caused by liver diseases. Discovering the liver illness is tough since its early phases, which will exhibit modest symptoms, are difficult to diagnose. Most often, cirrhosis disease doesn't manifest symptoms until an advanced stage, making it challenging to identify and diagnose. This paper shows different approaches to how they analyzed and visualized the performance. Medical issues can be resolved quickly and at a lower cost with the use of machine learning techniques. The major goal of this study is to increase the accuracy of result prediction and lower the price of diagnosis in the medical industry. As a result, we classified individuals with cirrhosis illness using several categorization methods. The biomedical field’s use of machine learning for the prognosis and diagnosis of cirrhosis disease has had a massive effect. In the biomedical area, machine learning provides a guarantee for better illness identification and prognosis, as well as for increasing the neutrality of the judgment process.