Title: Early identification of acute liver failure through machine learning algorithms

Authors: Preety Shoran; Esha Saxena; Meenakshi Yadav; Subash Harizan; Akhilendra Khare; Saket Thankur; Avneesh Vashistha

Addresses: Department of Computer Science and Engineering, CHRIST University, Banglore, India ' Department of Computer Science and Engineering, Guru Tegh Bahadur Institute of Technology, Delhi, India ' Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, India ' Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR, Ghaziabad, India ' Department of Computer Science and Engineering, Galgotias University, Greater Noida, India ' Department of Computer Science and Engineering, Galgotias University, Greater Noida, India ' Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR, Ghaziabad, India

Abstract: The liver plays a vital role in 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 very beneficial for effective treatment and diagnosis of patients' conditions. Machine learning algorithms create a great platform for analysing medical data that helps in improving disease detection procedures. This paper aims to get a better understanding of machine learning algorithms in the detection of liver disease. We will explore different machine-learning techniques for predicting liver disease detection. It uses various parameters as symptoms and calculates acute liver failure (ALF) based on the parameters and ALF decides whether the patient has a liver disease or not. Accuracy was calculated with various machine learning techniques, i.e., logistic regression classification, KNN classification, decision tree, random forest and support vector machine (SVM). Out of 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.

Keywords: acute liver failure; ALF; machine learning; feature extraction; liver disease.

DOI: 10.1504/IJAISC.2026.153144

International Journal of Artificial Intelligence and Soft Computing, 2026 Vol.10 No.1, pp.1 - 16

Received: 12 Mar 2025
Accepted: 19 Dec 2025
Published online: 23 Apr 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article