Application of Traditional Machine Learning Techniques for the Classification of Human DNA Sequences: A Comparative Study of Random Forest and XGBoost

Gregorius Airlangga

doi:10.32493/informatika.v9i1.39353

Authors

Gregorius Airlangga Atma Jaya Catholic University of Indonesia

DOI:

https://doi.org/10.32493/informatika.v9i1.39353

Keywords:

Machine Learning, DNA Sequence Classification, Random Forest, XGBoost, Genomic Data Analysis

Abstract

This study evaluates the performance of hybrid machine learning models, specifically Random Forest and XGBoost, in classifying human DNA sequences into seven functional classes. Utilizing advanced feature vectorization techniques, this research addresses the challenges of analyzing high-dimensional genomic data. Both models were trained and tested on a dataset of annotated human DNA sequences, with an emphasis on generalizability to new, unseen data. Our results indicate that the Random Forest model achieved an accuracy of 87.98%, slightly outperforming the XGBoost model, which recorded an accuracy of 87.06%. These findings underscore the effectiveness of employing traditional machine learning techniques coupled with innovative data preprocessing for predictive modeling in genomics. The study not only enhances our understanding of genomic functionalities but also suggests robust methodologies for future genetic research and potential applications in personalized medicine. The implications of these results for improving classification accuracy and the recommendations for integrating more complex algorithms are also discussed

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