Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Shoran, Preety; Yadav, Meenakshi; Saxena, Esha; Khare, Akhilendra; Harizan, Subash Title A name given to the resource AI-Driven Early Diagnosis of Acute Liver Failure: A Machine Learning Perspective Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE International Conference on Computer, Electronics, Electrical Engineering and their Applications, IC2E3 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC2E365635.2025.11167356" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC2E365635.2025.11167356</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018450164?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018450164?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shoran P., Christ University, School of Sciences, India; Yadav M., Galgotia College of Engineering &amp; Technology, Department of Information Technology, Gr. Noida, India; Saxena E., Guru Tegh Bahadur Institute of Technology, Department of Computer Science &amp; Engineering, Delhi, India; Khare A., Galgotia University, Department of Computer Science &amp; Engineering, Greater Noida, India; Harizan S., Galgotia Univesity, Department of Computer Science &amp; Engineering, Greater Noida, India Description An account of the resource The liver performs a valuable role in operating proper metabolism. This organ in the human body is responsible for maintaining and preserving overall health and well-being. However, when it fails to function optimally, it can cause severe and significant health complications. Liver diseases are multifactorial conditions that can be challenging to diagnose and treat. Early detection of any disease is beneficial for effective treatment and diagnosis of patients' conditions. Machine Learning algorithms create a great platform for analyzing medical data that helps improve disease detection procedures. This paper aims to get a better understanding of ML algorithms for detecting diseases associated with the liver. The paper tries to explore various machine learning techniques for predicting accurate liver diseases. It uses various parameters as symptoms and calculates ALF (Acute Liver Failure) based on the parameters and ALF predicts in-case the person is suffering from a Liver disease or not. Accuracy was calculated with various ML techniques i.e. Logistic Regression Classification, KNN Classification, Decision Tree, Random Forest and Support Vector Machine. Among all these, Logistic Regression was found to be most effective in identifying and predicting the outcome of the dataset compared to other algorithms. SVM has a higher cross-validation score but Accuracy, precision and recall are very low thus, cannot select this model. 2025 IEEE. Subject The topic of the resource Acute Liver Failure; Feature Extraction; Liver Disease; Machine Learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152439-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online