Machine Learning Application in Liver Disease Prediction

Abstract

Liver disease stands out as a prominent cause of mortality in India according to the World Health Organization, as well as on a global scale. The field of machine learning has emerged as a highly promising domain within the healthcare sector. Within the realm of medicinal industry, Computer Aided Diagnosis (CAD) represents a developing area of exploration. Extensive research has been conducted on the analysis of liver disease leveraging machine learning, highlighting advancements in the accuracy of disease detection and diagnosis. Through the utilization of machine learning, computers are equipped to assimilate knowledge and draw inferences from historical data. Consequently, computers can autonomously engage in self-learning processes, without the need for explicit programming by human developers. The present study offers an overview of machine learning methodologies employed in the context of liver disease, utilizing diverse datasets including liver function test data, Ultrasonic images (US), Computerized Tomography (CT) images, Magnetic Resonance Imaging (MRI).