Forthcoming Articles

International Journal of Artificial Intelligence and Soft Computing

International Journal of Artificial Intelligence and Soft Computing (IJAISC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Artificial Intelligence and Soft Computing (One paper in press)

Regular Issues

  • Early Identification of Acute Liver Failure through Machine Learning Algorithms   Order a copy of this article
    by Preety Shoran, Esha Saxena, Meenakshi Yadav, Subash Harizan, Akhilendra Khare, Saket Thankur, Avneesh Vashistha 
    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; Machine Learning; Feature Extraction; Liver Disease.
    DOI: 10.1504/IJAISC.2026.10076041
     

 

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