Abstract
Hepatitis is inflammation of the liver’s tissue and is often brought on by an infection. Several research efforts have been made to create machine learning algorithms for the diagnosis of hepatitis disease. However, the link between hepatitis and its symptoms is seldom discussed. The primary goal of this research was to describe in detail a dataset of hepatitis symptoms culled from actual cases. The study authors also suggested creating a standalone classification platform that uses random forest support vector machine algorithms and decision tree to differentiate between healthy and diseased individuals. To do this, we would choose applicable variables and enclose them in a malleable wrapper. Some traits have been proven to correlate highly with a hepatitis diagnosis. This article describes a technique that has the potential to enhance early-stage hepatitis detection, which might lessen the disease’s devastating impact on human life. Remember that of the three methods RF achieved the best accuracy and maintained its superiority across all datasets with very little variation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Scientific Methods in Intelligence Engineering Networks
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
