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

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.