Detail Cantuman Kembali
PROSIDING: IMPLEMENTATION OF THE BAT ALGORITHM AND ENSEMBLE METHODS FOR TUBERCULOSIS DETECTION IN HIV PATIENTS BASED ON GENE EXPRESSION DATA
- Bagikan:
Tuberculosis (TB) remains one of the leading causes of mortality worldwide, particularly among individuals with Human Immunodeficiency Virus (HIV). Conventional diagnostic methods are often costly, time-consuming, and insufficiently sensitive for HIV patients. This study proposes a hybrid TB detection system based on gene expression data by integrating the Bat Algorithm for feature selection with ensemble learning classifiers (Random Forest, AdaBoost, and XGBoost). Using 44 peripheral blood RNA samples from the Gene Expression Omnibus (GSE50834), the Bat Algorithm effectively reduced dimensionality and improved classification performance. AdaBoost achieved the best results with an accuracy of 89% and an F1-score of 0.86. Although evaluation was conducted using an 80/20 train-test split, we acknowledge the need for more robust validation such as k-fold cross-validation or bootstrapping in future work. These findings highlight the potential of bioinformatics-driven approaches to enhance early TB detection in HIV patients and provide a promising complement to conventional diagnostics.
Keywords— Tuberculosis, HIV, Gene Expression, Bat Algorithm, Ensemble learning
020 HAR I R.1
NONE
Thesis
Bahasa Inggris
UNIVERSITAS TEKNOLOGI DIGITAL INDONESIA (UTDI)
2025
Yogyakarta
020 MTI 2025
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