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Applying Various Machine Learning Techniques for Early Diagnosis of Breast Cancer

by Mohamed Shaban Abden, Mostafa Ali Elmasry, Kamel Hussein Rahouma
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Journal of Advanced Artificial Intelligence
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 3
Year of Publication: 2024
Authors: Mohamed Shaban Abden, Mostafa Ali Elmasry, Kamel Hussein Rahouma
10.5120/jaai202414

Mohamed Shaban Abden, Mostafa Ali Elmasry, Kamel Hussein Rahouma . Applying Various Machine Learning Techniques for Early Diagnosis of Breast Cancer. Journal of Advanced Artificial Intelligence. 1, 3 ( Dec 2024), 14-22. DOI=10.5120/jaai202414

@article{ 10.5120/jaai202414,
author = { Mohamed Shaban Abden, Mostafa Ali Elmasry, Kamel Hussein Rahouma },
title = { Applying Various Machine Learning Techniques for Early Diagnosis of Breast Cancer },
journal = { Journal of Advanced Artificial Intelligence },
issue_date = { Dec 2024 },
volume = { 1 },
number = { 3 },
month = { Dec },
year = { 2024 },
pages = { 14-22 },
numpages = {9},
url = { https://jaaionline.phdfocus.com/archives/volume1/number3/applying-various-machine-learning-techniques-for-early-diagnosis-of-breast-cancer/ },
doi = { 10.5120/jaai202414 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-31T21:43:40.419681+05:30
%A Mohamed Shaban Abden
%A Mostafa Ali Elmasry
%A Kamel Hussein Rahouma
%T Applying Various Machine Learning Techniques for Early Diagnosis of Breast Cancer
%J Journal of Advanced Artificial Intelligence
%V 1
%N 3
%P 14-22
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cancer disease is a category of diseases distinguished as an uncontrolled increase and extension of unnatural cells within the body, often caused by genetic mutations and various risk factors. Breast cancer (BC) stands as a common cancer forms. The early detection through timely examination and treatment greatly improves the chances of a successful outcome. To enhance early detection and improve treatment outcomes, a gene expression data set was used, but the curse of dimensionality appears when trying to analyze such data. We aim to create an accurate model. So, it is important to filter this noise and lower the dimensions in the microarray data, which is considered a mandatory step. In this study, we conducted experiments for the early identification of breast cancer. For this task, we used breast cancer microarray data to classify patients. First, the dataset was normalized using the min-max scalar technique, and then its features were obtained using Binary Harris Hawks Optimization (BHHO). The application of machine learning models like k-nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR), decision tree (DT), and neural network (NN) are investigated. Our experiments show that DT outperformed the other models producing the highest performance across Van't Veer dataset.

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Index Terms

Computer Science
Information Sciences

Keywords

Breast cancer classification. Microarray data. Binary Harris Hawks Optimization (BHHO)

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