A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation - Article

Aug 4, 2026

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Breast cancer is a leading cause of death among women, and early and accurate diagnosis remains critical. This study classifies breast cancer microarray data into normal and relapse classes using machine learning methods. Gene expression datasets GSE45255 and GSE15852 are integrated into a single dataset. Three algorithms—random forest, extra trees, and support vector machine—are tuned via grid search cross validation for hyperparameter optimization. The tuned SVM achieves the highest accuracy at 97.78%, while future work is recommended to add feature selection to improve performance further.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Breast cancer background and need for early diagnosis

DNA microarray and gene expression data

Challenges in microarray classification

The Machine Learning Algorithms

Supervised classification workflow

Random forest, extra trees, and SVM

Grid search cross validation for hyperparameters

A case study of microarray breast cancer classification using machine learning algorithms with grid search cross validation Nursabillilah Mohd Ali1,2, Rosli Besar2, Nor Azlina Ab Aziz2 1Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia 2Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia Article history: Received Sep 26, 2022 Revised Nov 2, 2022 Accepted Nov 23, 2022 Keywords: Microarray Breast Cancer Grid SearchCV Classification Corresponding Author: Breast cancer is one of the leading causes of death and most frequently diagnosed cancer amongst women. Annually, almost half a million women do not survive the disease and die from breast cancer. Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, and gradually improving its accuracy. In this work, simple machine learning methods are used to classify breast cancer microarray data to normal and relapse. The data is from the gene expression omnibus (GEO) website namely GSE45255 and GSE15852 . These two datasets are integrated and combined to form a single dataset. The study involved three machine learning algorithms, random forest (RF), extra tree (ET), and support vector machine (SVM) . Grid search cross validation (CV) is applied for hyperparameter tuning of the algorithms. The result shows that the tuned SVM is best among the tested algorithms with accuracy of 97.78% . In the future it is recommended to include feature selection method to get the optimal features and better classification accuracies. This is an open access article under the CC BY-SA license. Nursabillilah Mohd Ali Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka Durian Tunggal, Melaka, Malaysia [Email: nursabillilah@utem.edu.my](Email: nursabillilah@utem.edu.my) Article Info ABSTRACT 1. INTRODUCTION Breast cancer is the most prevalent disease among women worldwide. Many women are affected by this life-threatening cancer. It is the second biggest factor in female cancer-related fatalities [1], [2] . Breast cancer is a malignant tumor caused by the breast’s cells growing and dividing out of control thus creating a lump of tissues. However, not all lumps are cancerous, benign tumors are non-cancerous growths that are treatable with medication and are not life-threatening [2] . Whereas malignant tumors are cancerous growths that can be fatal if left untreated. Early diagnosis is important if such a lump appears in the patients’ breast, they must discuss with a medical doctor for early diagnoses and medical treatment [3], [4] . One of the most essential technologies in bioinformatics research is the gene chip, commonly known as the DNA microarray [5]–[7] . A great amount of biological information is available in gene expression microarray data. This is contributed by the rapid development of sequencing technologies [5], [6] . Breast cancer gene expression profiles are among information available in microarray data, which is important in prognosis of breast cancer patients [7]-[9] . The expression variables in the microarray dataset are often organised as a MxN matrix, with column containing several features [10] (also known as genes) and each row representing a sample, as illustrated in Figure 1 [6] . In recent years, researchers have shown a great deal of interest in the detection and classification of cancer through microarray data using machine learning algorithms. The classification of microarray data classifies cancer samples according to their class based on their gene expression profiles. Meanwhile, machine learning is a subset of artificial intelligence (AI) that enables systems to learn from the training data and get better over time. According to Almugren and Alshamlan [8], a machine learning algorithm known as support vector machine (SVM) is hybridized with firefly algorithm for classification of

FAQs

What datasets are used for the microarray breast cancer study?

The study uses gene expression datasets from GEO: GSE45255 and GSE15852, which are integrated into a single dataset.

Which machine learning algorithms are evaluated for classification?

Random forest (RF), extra trees (ET), and support vector machine (SVM) are evaluated to classify samples into normal and relapse.

How are model hyperparameters selected in this study?

Hyperparameters are tuned using grid search cross validation (CV), applied to each algorithm.

Aug 4, 2026

Upload by Jiven