Published Paper Details:

The human body relies on the healthy liver to perform over 500 essential functions, and any dysfunction can have severe consequences, potentially leading to fatality. Timely identification and treatment of liver diseases can significantly enhance the chances of survival. In this regard, machine learning (ML) emerges as a valuable resource that can aid healthcare experts in diagnosing hepatic patients. The conventional ML framework encompasses various techniques such as data pre-processing, feature extraction, and classification, which collectively contribute to accurate diagnoses. Liver disease is a matter of great concern in the global health landscape, impacting a vast number of people across the world. The timely and precise identification of liver disease plays a pivotal role in ensuring successful treatment and averting potential complications. Over the past few years, the advent of machine learning has revolutionized the healthcare sector, empowering the creation of predictive models that aid in diagnosing and forecasting diverse medical ailments, liver disease being one of them. The proposed approach incorporates several ML algorithms, including logistic regression (LR), random forest (RF), and confusion matrix. The results indicate that the suggested system has the potential to complement the diagnosis of liver disease made by a physician