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Updated: Oct 16, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Brain Cancer Prediction Based on Novel Interpretable Ensemble Gene Selection Algorithm and Classifier.

Abdulqader M Almars¹, Majed Alwateer¹, Mohammed Qaraad^2,3

¹College of Computer Science and Engineering, Taibah University, Yanbu 46411, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|October 23, 2021

Summary

This summary is machine-generated.

This study introduces a hybrid model for brain tumor classification using feature selection and optimized gradient boosting, achieving high accuracy. The findings link gene expression patterns to tumor types, aiding diagnosis and treatment.

Keywords:

brain cancer classification ensemble methods feature selection gene expression data gene selection hyperparameter optimization

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Area of Science:

Computational biology
Bioinformatics
Machine learning in oncology

Background:

Human brain tumors arise from abnormal cell growth, necessitating accurate classification for prognosis and treatment.
Cancer microarray data presents challenges due to high dimensionality (many gene expression levels) and limited samples.
Existing classification studies often lack biological interpretability of the genes involved.

Purpose of the Study:

To develop a novel hybrid model for brain tumor classification.
To integrate feature selection (mRMR) and hyperparameter optimization for gradient boosting classifiers.
To bridge the gap between cancer classification accuracy and biological interpretation of implicated genes.

Main Methods:

Proposed a hybrid model combining Minimum Redundancy Maximum Relevance (mRMR) feature selection with distributed hyperparameter optimization for gradient boosting.
Evaluated the model using Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) classifiers.
Compared optimized CatBoost and XGBoost performance across multiple cross-validation folds.

Main Results:

The optimized CatBoost classifier outperformed optimized XGBoost in AUC, sensitivity, and specificity across several cross-validation folds.
Optimized CatBoost achieved 0.91±0.12 accuracy, significantly improving upon the unoptimized version (0.81±0.24).
Hybrid algorithms enhanced NB, RF, and SVM accuracy, with SVM and RF reaching 0.97±0.08, surpassing NB (0.91±0.12).

Conclusions:

The proposed hybrid model significantly improves brain tumor classification accuracy.
Optimized gradient boosting methods, particularly CatBoost, demonstrate superior performance.
The study successfully links classification findings to relevant biological insights from gene expression data.