Harmonizing Machine Learning Algorithms for Enhanced Liver Disease Prediction: A Comparative Study of Accuracy and Efficacy
Year: 2025
Type: article
Abstract: In this review paper, various machine learning techniques applied to predict liver disease have been studied. Non-Alcoholic Fatty Liver Disease (NAFLD) is a leading cause of global mortality, which affects millions of people worldwide. The diagnosis of NAFLD can be both costly and complex. Therefore, this research aims to reduce these challenges by assessing the capability of various machine learning models to prevent NAFLD, thereby reducing the overall cost of diagnosis. We have used 3 classification algorithms-Logistic Regression, Support Vector Machine, along the Random Forest technique for enhanced prediction accuracy. Our study evaluates these models based on metrics like accuracy, precision, recall, and F1 score. Also, we are inclined towards developing a hybrid model to further improve efficiency. Based on our study of various other papers, it was found that integrating-ing multiple Machine Learning (ML) techniques helps to significantly enhance the performance, offering an efficient approach towards the diagnosis.
DOI: 10.14419/vg1c4b90
Authors: Laxminath Tripathy, Dharmveer Kumar Yadav, Brijesh Kumar Singh
Institution: Siksha O Anusandhan University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4413310744