ItemConference Paper

AI-Driven Early Diagnosis of Acute Liver Failure: A Machine Learning Perspective

Title
AI-Driven Early Diagnosis of Acute Liver Failure: A Machine Learning Perspective
Creator
Shoran, Preety; Yadav, Meenakshi; Saxena, Esha; Khare, Akhilendra; Harizan, Subash
Description
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.
Source
2025 IEEE International Conference on Computer, Electronics, Electrical Engineering and their Applications, IC2E3 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Acute Liver Failure; Feature Extraction; Liver Disease; Machine Learning
Coverage
Shoran P., Christ University, School of Sciences, India; Yadav M., Galgotia College of Engineering & Technology, Department of Information Technology, Gr. Noida, India; Saxena E., Guru Tegh Bahadur Institute of Technology, Department of Computer Science & Engineering, Delhi, India; Khare A., Galgotia University, Department of Computer Science & Engineering, Greater Noida, India; Harizan S., Galgotia Univesity, Department of Computer Science & Engineering, Greater Noida, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152439-5;
Format
online
Language
English
Type
Conference paper

Citation

Shoran, Preety; Yadav, Meenakshi; Saxena, Esha; Khare, Akhilendra; Harizan, Subash, “AI-Driven Early Diagnosis of Acute Liver Failure: A Machine Learning Perspective,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25859.

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