Artificial Neural Network based Machine Learning Technique for Liver Disease Prediction

Rajni Sambyal; Deepak Chandra Uprety

Author(s)

Rajni Sambyal Department of Computer Science & Engineering, Shri Venkateshwara University,Gajraula, UP,India

Deepak Chandra Uprety Department of Computer Science & Engineering, Shri Venkateshwara University,Gajraula, UP,India

Abstract

Liver diseases, such as cirrhosis, hepatitis, and fatty liver disease, have become an increasing global health concern in recent decades. The rise in these conditions poses a serious threat to public health and places a significant strain on healthcare systems. Early detection is critical in managing liver diseases effectively, but the complexity of the contributing factors and the variability in patient data make accurate predictions challenging. Researchers are confronted with the task of analyzing vast and heterogeneous datasets containing key clinical and biochemical parameters, including liver function tests, demographics, and lifestyle factors. To address this challenge, sophisticated machine learning techniques are needed to uncover meaningful patterns in the data that can reliably predict the onset of liver diseases and assist in timely diagnosis and prevention. This study proposes an Artificial Neural Network (ANN)based solution that leverages a comprehensive dataset encompassing critical parameters such as age, gender, total bilirubin, direct bilirubin, and alkaline phosphatase levels. By processing these inputs, the ANN model is designed to identify subtle patterns and make accurate predictions for early liver disease detection. Preliminary results indicate that this deep learning-based approach has the potential to outperform traditional diagnostic methods, offering a faster, more accurate tool for healthcare providers. The integration of machine learning into liver disease diagnosis not only enhances predictive accuracy but also holds the promise of revolutionizing how these diseases are detected and managed, ultimately improving patient outcomes and reducing healthcare burdens.

Publication Details

Page(s) 934-941

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: S1, Year: 2024

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Keywords

KNN, SVM, Machine learning, ANN, Liver diseases, LR

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