Predictive Analysis of Liver Disease Using Random Forest Machine Learning Algorithm

The proposed system’s objective is to improve the performance of diagnosing liver diseases through machine learning by using the Random Forest algorithm. Such systems accommodate a detailed database that comprises of several parameters of the patient’s health status, through which important factors that cause liver disease may be determined. The analysis utilizes Random Forest, a feature selection technique that boosts the efficacy of the model’s results in determining the outcomes of liver diseases with accuracy and recall. The training is conducted to reduce the component’s error rate as well as to determine the ability of the system to perform complex tasks. Likewise, the proposed solution also focuses on the easy-to-use front-end for the target groups – the healthcare organizations – so the solution perfectly fits into their day-to-day work. In sum, this system is designed to become an innovative, cutting edge, and evidence-based tool for the notification and prevention of the liver disease in its early stage so as to enhance patients’ outcomes and make more efficient use of the health care facilities.