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

Authors

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

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Issue

Article ID

SPP-2021-2G-03

Section

Biological and Medical Physics

Published

2021-10-02

How to Cite

[1]
NC Astrologo, J Gaudillo, RM Roxas-Villanueva, B Tiangco, and J Albia, Machine learning approach to the classification of hepatitis B surface antigen seroclearance in hepatitis B virus, Proceedings of the Samahang Pisika ng Pilipinas 39, SPP-2021-2G-03 (2021). URL: https://proceedings.spp-online.org/article/view/SPP-2021-2G-03.