Authors : S. Selvakumar; S Sanjay

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/2s3nrajz

Scribd : https://tinyurl.com/49sxurha

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1960

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Liver disease continues to be a major global health concern, accounting for a considerable portion of global mortality. It results in a variety of symptoms such aberrant nerve function, blood in the cough or vomit, renal and liver failure, jaundice, and liver encephalopathy. It is caused by a myriad of variables that influence the liver, including obesity, untreated hepatitis infection, and alcohol misuse. In order to effectively treat liver infections, early detection is essential, and sensor- based medical technology is frequently used in modern medical procedures to identify illnesses. But diagnosing a condition can be expensive and difficult. Thus, the purpose of this paper is to compare the effectiveness of different machine learning algorithms in order to judge how well they function and have what potential to categorize liver diseases.

Keywords : Random Forest, Decision Tree, Support Vector Machine (SVM), Logistic Regression, and Categorization.

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