Machine learning approach to the classification of hepatitis B surface antigen seroclearance in hepatitis B virus

Authors

  • Nicole Cathlene Astrologo Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
  • Joverlyn Gaudillo Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños and Domingo AI Research Center
  • Ranzivelle Marianne Roxas-Villanueva Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños
  • Beatrice Tiangco National Institutes of Health, University of the Philippines Manila and Department of Medicine, The Medical City
  • Jason Albia Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños and Domingo AI Research Center

Abstract

This study used an integrated machine learning (ML) classification technique to classify patients with or without seroclearance of hepatitis B surface antigen (HBsAg) using single nucleotide polymorphism (SNP). Bayesian optimization was employed for tuning the hyperparameter values of the random forest (RF) and support vector machine (SVM) models. Results showed that the incorporation of RF as a feature selection method to the SVM classifier yielded higher performance metrics than solely using the baseline models, with 80% accuracy, 79% precision, 80% sensitivity, and area under the curve (AUC) of 0.8. This paper demonstrated that the integration of ML models led to a more suitable analysis of SNP profiles for disease risk prognosis.

SPP2021 Proceedings Cover

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Published

2021-10-02

Issue

2021: Proceedings of the 39th Samahang Pisika ng Pilipinas Physics Conference

Section

Biological and Medical Physics

License

By submitting their manuscript to the Samahang Pisika ng Pilipinas (SPP) for consideration, the Authors warrant that their work is original, does not infringe on existing copyrights, and is not under active consideration for publication elsewhere.
Upon acceptance of their manuscript, the Authors further agree to grant SPP the non-exclusive, worldwide, and royalty-free rights to record, edit, copy, reproduce, publish, distribute, and use all or part of the manuscript for any purpose, in any media now existing or developed in the future, either individually or as part of a collection.
All other associated economic and moral rights as granted by the Intellectual Property Code of the Philippines are maintained by the Authors.

How to Cite

[1]
“Machine learning approach to the classification of hepatitis B surface antigen seroclearance in hepatitis B virus”, Proc. SPP, vol. 39, no. 1, pp. SPP–2021, Oct. 2021, Accessed: Mar. 30, 2026. [Online]. Available: https://proceedings.spp-online.org/article/view/SPP-2021-2G-03