Liver Disease Recognition Using Machine Learning

Abstract-For more effective treatment, early diagnosis of liver disease is crucial. Detecting liver disease in its early stages is challenging due to its subtle symptoms, often becoming apparent only in advanced stages. This research leverages machine learning techniques to address this issue by enhancing liver disease detection. The primary objective is to differentiate between liver patients and healthy individuals using classification algorithms. Liver disease has seen a global increase in prevalence in the 21st century, with nearly 2 million annual deaths attributed to it according to recent surveys. It accounts for 3.5% of global deaths [1]. Early diagnosis and treatment can significantly improve outcomes for patients with chronic liver disease, which is among the most fatal illnesses. The advancement of artificial intelligence, including various machine learning algorithms like Regression, Support vector machine, KNN, and Random Forest, offers the potential to extend the lifespan of individuals with Chronic Liver Disease (CLD).