What question did this study set out to answer?

The aim is to improve breast cancer classification using machine learning algorithms based on gene expression patterns.

June 10, 2026

Gene Expression-Based Cancer Classification using Machine Learning

Key Points

The aim is to improve breast cancer classification using machine learning algorithms based on gene expression patterns.
Implemented a machine learning model utilizing the BC-TCGA dataset for breast cancer classification.
Conducted missing value imputation using Imperative SVD and performed feature selection with an AGA-based algorithm and MIM.
Trained classifiers including SVMs, RF, and XGBoost on a selected set of genes.
Achieved classification accuracy above 90%.
Demonstrated that feature selection significantly improves cancer prediction based on gene expression.
Highlighted the effectiveness of machine learning in handling high-dimensional data for breast cancer diagnosis.

Abstract

Breast cancer is still one of the primary causes of mortality among women, making it critical for timely diagnosis and improved treatment. Molecular pattern analysis via gene expression provides an excellent opportunity for discovering patterns that correspond to cancer development; nevertheless, high dimensionality poses some difficulties for classification. In order to cope with the issue, the current study suggests implementing a machine learning-driven model for breast cancer classification based on BC-TCGA dataset. Preprocessing was conducted through missing value imputation with Imperative SVD using normalization. Feature selection was done by means of an AGA-based algorithm combined with MIM. As a result, various classifiers such as SVMs, RF, Logistic Regression, and XGBoost were trained using a selected set of genes. The suggested pipeline produced accuracy above 90. This work demonstrates that implementation of both feature selection and machine learning contributes considerably to breast cancer prediction with gene expression.

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