Non-Alcoholic fatty liver disease prediction with feature optimized XGBoost model

Abstract

Non-alcoholic fatty liver disease (NAFLD) is an expanding health threat, posing significant risks for long-term complications. Early detection and intervention are crucial, but traditional diagnostic methods can be expensive and invasive. This study investigates the utilization of machine learning models for predicting liver diseases from various out-sourced datasets. .We employed Decision Trees, Random Forests, and Support Vector Machines (SVMs) to predict NAFLD based on various clinical and demographic features. Model performance was evaluated by calculating accuracy, precision, deviation and accuracy-score. All these models achieved promising accuracy levels, ranging from 80% to 90%, showcasing their potential for NAFLD prediction. Among them, XGBoost demonstrated the highest performance, with an accuracy of 90% and more. This study demonstrates the effectiveness of machine learning models in predicting NAFLD with high accuracy using readily available data. Further research with larger sized and more varied datasets will vindicate these models for real-world application in clinical settings