Background: Medulloblastoma (MB) is the most prevalent malignant brain tumor in children, characterized by substantial molecular heterogeneity across its subgroups. Accurate classification is pivotal for personalized treatment strategies and prognostic assessments. Procedure: This study utilized machine learning (ML) techniques to analyze RNA sequencing data from 70 pediatric medulloblastoma samples. Five classifiers, K-nearest Neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB), were employed to predict molecular subgroups based on gene expression profiles. Feature selection identified gene subsets of varying sizes (750, 75, and 25 genes) to optimize classification accuracy. Results: Initial analyses with the complete gene set lacked discriminative power. However, reduced feature sets significantly enhanced clustering and classification performance, particularly for Group 3 and Group 4 subgroups. The RF, KNN, and SVM classifiers consistently outperformed the DT and NB classifiers, achieving classification accuracies exceeding 90% in many scenarios, especially in Group 3 and Group 4. Conclusions: This study highlights the efficacy of ML algorithms in classifying medulloblastoma subgroups using gene expression data. The integration of feature selection techniques substantially improves model performance, paving the way for enhanced personalized approaches in medulloblastoma management.

The authors have declared no competing interest.

This study did not receive any funding

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