Detection and Diagnosis of Hepatitis Virus Infection Based on Human Blood Smear Data in Machine Learning Segmentation Technique

This work aims to choose the right method for hepatitis diagnosis and prevention and hepatitis treatment. Life expectancy estimation in Hepatitis patients seems to be very low. A comparative in this assignment analysis of different machine learning instruments., and they tried out neural networks. The metric's achievement is based on the accuracy rate and an error in the average rectangle. The Machine Learning Algorithms (ML)., such as K Nearest Neighbor (K-NEAREST NEIGHBOR)., Naive Bayes Algorithm and Support Vector Machines (SVM)., were considered classification and prediction tools for Data Segmentation for Detection and diagnosing Hepatitis disease. On the above algorithms, a short study was performed. Focusing on the predictive precision of disease diagnosis.