A Machine Learning Framework for Hepatic Health Analysis

Abstract

Liver diseases such as Hepatitis, Cirrhosis, Fatty Liver Disease (NAFLD), and Cholestasis are the main causes of world-wide health burden due to their non-symptomatic presentation and diagnosis at late stages. Correct diagnosis at an earlier stage is crucial to have improved patient outcomes. The present study presents a Machine Learning Framework to characterize the liver health by using supervised methods to categorize and sub-type liver pathology. The data is based on Kaggle's Liver Patient Dataset and includes clinical and biochemical parameters such as Age, Gender, levels of Bilirubin, Alkaline

Phosphatase, AST, SGPT, levels of Albumin, and the A/G Ratio. A standard data pre- processing routine was used to clean up the missing values, remove outliers, add new

result column and normalize features to prepare and transform the data to feed into models. Its rule-based labeling system identified the categories of the diseases and then several machine learning classifiers were trained on the dataset by employing Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and an ensemble Voting Classifier. The RF and the KNN demonstrated extremely high accuracy (65-99%), but the Voting Classifier demonstrated higher robustness by using ensemble learning. This rule-based system was employed to further classify Hepatitis cases into subtypes: Acute Viral (A/E), Chronic Hepatitis B, Hepatitis C, Alcoholic Hepatitis, Autoimmune Hepatitis, and NASH (Fatty Hepatitis). The hybrid system has been implemented as an online interactable web app through Streamlit with real-time liver disease and Hepatitis subtype predictions so it is readily accessible to clinicians, researchers, and patients. This system offers faster early detection and a better liver health diagnostic tool.