Predictive Analytics and Visualization in Hepatitis B Research Using Machine Learning Techniques

DOI

10.2991/978-94-6463-978-0_56How to use a DOI?

Keywords

Hepatitis B; Machine Learning; Classification; Healthcare Analytics; Biomedical Data

Abstract

Hepatitis B is a potentially life-threatening liver infection caused by the Hepatitis B Virus (HBV). Despite the availability of vaccines, the disease remains a global health challenge. This research applies data-driven approaches using Machine Learning (ML) models to analyze patient data and uncover patterns for effective diagnosis and management. Using patient records from a comprehensive dataset, the research has been conducted on preprocessing, feature extraction, and classification using algorithms such as Random Forest, Support Vector Machines (SVM), Deep NeuralNetwork (DNN), SHAP Summary Plot and Logistic Regression. A study was conducted to visualize the relationship between clinical parameters and disease status to identify risk indicators. The proposed methodology includes pseudocode and a flowchart to illustrate the machine learning pipeline. The results demonstrate that Random Forest achieved the highest accuracy of 96 Percent, significantly outperforming other models. Graphs show clear patterns of elevated liver enzymes and bilirubin in infected patients. This paper also surveys literature on Artificial Intelligence(AI) in healthcare, particularly for infectious diseases, and discusses limitations and ethical concerns of clinical AI applications. These findings provide actionable insights for early diagnosis, potentially reducing complications like cirrhosis or liver cancer. The study suggests that machine learning can be a critical tool in public health strategies against HBV. Future research may extend this to real-time diagnostics or combine it with genomic data for precision medicine.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Danish Ahmed
AU  - S. Manojna
AU  - Srushti Hanagandi
AU  - Vrushabh Kumatgi
AU  - Rajashri Khanai
AU  - Salma Shahapur
PY  - 2025
DA  - 2025/12/31
TI  - Predictive Analytics and Visualization in Hepatitis B Research Using Machine Learning Techniques
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 664
EP  - 673
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_56
DO  - 10.2991/978-94-6463-978-0_56
ID  - Ahmed2025
ER  -