Komparasi Algoritma Support Vector Machine, Random Forest, dan XGBoost: Prediksi Penyakit Huntington Berdasarkan Data Ekspresi Gen

Reza Zaynury Winata (Universitas Teknokrat Indonesia, Bandar Lampung)
Erliyan Redy Susanto (Universitas Teknokrat Indonesia, Bandar Lampung)

Building of Informatics, Technology and Science
Vol 8 No 2 (2026): September 2026

Huntington's disease is a progressive neurodegenerative disorder caused by mutations in the Huntingtin (HTT) gene, and early diagnosis remains an ongoing challenge. Leveraging gene expression data through machine learning holds potential for improving the prediction accuracy of this disease; however, the characteristics of microarray data very high feature dimensionality combined with a limited sample size (small-n large-p) together with class imbalance, increase the risk of overfitting and data leakage. This study compares the performance of Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost) in predicting Huntington's disease status using the GSE135589 microarray dataset. The research pipeline includes data standardization, feature selection (SelectKBest based on the ANOVA F-test), class balancing (SMOTE), and hyperparameter tuning via GridSearchCV with Stratified 5-Fold Cross-Validation, in which the number of features (k) is also tuned as a nested hyperparameter alongside other model parameters to prevent information leakage between the training and test data. All preprocessing steps were applied exclusively to the training data (fit-on-train, transform-on-test) to ensure performance estimates free from optimistic bias. The results show that the linear-kernel SVM achieved the best performance, with an accuracy of 88.89%, precision of 89.66%, recall of 96.30%, F1-score of 92.86%, and ROC-AUC of 90.95%, outperforming Random Forest and XGBoost on the independent test set. These findings indicate that regularized linear approaches outperform tree-based models under conditions where feature dimensionality far exceeds sample size. The main novelty of this study lies in tuning the number of features (k) as a nested hyperparameter within the cross-validation scheme rather than as a separate step prior to model training, as is common in most comparable studies, thereby producing a stricter, leakage-free generalization estimate, while also serving as the first comprehensive benchmark comparing the three algorithms specifically within the domain of Huntington's disease gene expression data.