Cirrhosis Cancer Detection Using Machine Learning Approach

Abstract

Machine learning has become an important tool in knowledge and data engineering, particularly in predicting outcomes based on existing data. One of the most commonly used machine learning techniques is classification, which involves learning patterns from an existing dataset and then applying them to a new dataset to make predictions. However, some classification algorithms have limited accuracy. This study proposes a new method called Supervised Learning Technique, which achieves higher accuracy than existing algorithms like Logistic Regression, SVM, DT, GNB and RF. KNN classifier got 91% accuracy to classify diseases.The researchers created a user-friendly web application to allow users to input data and get results. The study used an Indian liver disease dataset to demonstrate how the regression technique can improve prediction accuracy for liver disease. The goal of the study was not only to increase accuracy but also to demonstrate the usefulness of the algorithm for predicting diseases at an early stage.