Academia Open
Vol 10 No 1 (2025): June (In Progress)
Computer Science

Developing a Prediction Model to Identify Blood Types Most Susceptible to Viral Hepatitis Based on the CRISP-DM Methodology
Mengembangkan Model Prediksi untuk Mengidentifikasi Golongan Darah yang Paling Rentan terhadap Virus Hepatitis Berdasarkan Metodologi CRISP-DM


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  • Esraa Hameed Kamel*
Esraa Hameed Kamel
Waist University, college of finearts ,waist , Iraq *

(*) Corresponding Author
Picture in here are illustration from public domain image or provided by the author, as part of their works
DOI: https://doi.org/10.21070/acopen.10.2025.10740
Published March 6, 2025
Keywords
  • Random Forest algorithm,
  • Hepatitis B and C,
  • KNN algorithm,
  • Blood Groups,
  • Decision Tree algorithm,
  • support vector machine algorithm,
  • ,XG-Boost algorithm,
  • neural network algorithm
    • ...More
    Less
How to Cite
Kamel, E. H. (2025). Developing a Prediction Model to Identify Blood Types Most Susceptible to Viral Hepatitis Based on the CRISP-DM Methodology. Academia Open, 10(1), 10.21070/acopen.10.2025.10740. https://doi.org/10.21070/acopen.10.2025.10740

Copyright (c) 2025 Esraa Hameed Kamel

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

General Background: Viral hepatitis is a prevalent disease worldwide, with hepatitis B and C posing significant public health challenges. While most cases resolve naturally, chronic infections contribute to severe complications. Specific Background: Genetic predisposition, including blood type, has been hypothesized as a risk factor for viral hepatitis; however, its role remains unclear. Knowledge Gap: Limited studies have analyzed the association between ABO blood groups and susceptibility to hepatitis B and C using machine learning techniques. Aims: This study aims to determine the blood groups most susceptible to hepatitis B and C by applying advanced machine learning models. Results: Using a dataset of 500 patients and CRISP-DM methodology, the findings indicate that blood type B has the highest susceptibility (38% infection rate), while type O shows the lowest risk (15%). Statistical analysis (Chi-square, p < 0.01) confirms a significant correlation between blood group B and hepatitis infection. The XG-Boost model achieved the highest predictive accuracy (91%), identifying blood type B as the second most influential risk factor after age. Novelty: This study provides empirical evidence linking genetic factors, particularly blood type B, with hepatitis susceptibility using data-driven approaches. Implications: The findings highlight the importance of blood type screening in high-risk populations and the necessity of targeted prevention strategies.

Highlights:

 

  1. Blood type may influence susceptibility to hepatitis B and C.
  2. Blood type B shows highest risk; XG-Boost model achieves 91% accuracy.
  3. Blood type screening aids early detection and targeted prevention strategies.

Keyword: Random Forest algorithm,  Hepatitis B and C, KNN algorithm, Blood Groups, Decision Tree algorithm, support vector machine algorithm ,XG-Boost algorithm, neural network algorithm.

 

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