A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection

Aug 5, 2026

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Breast cancer demands early detection to enable effective treatment, and AI-driven diagnosis has gained traction for improving accuracy while reducing false positives. This study proposes a hybrid approach combining 1D CNN for feature extraction with machine learning classifiers including XGBoost, random forests, decision trees, support vector machines, and k-nearest neighbors to classify samples as benign or malignant. Using the Wisconsin breast cancer (WBC) dataset, the XGBoost model with 1D CNN features achieves 98.24% accuracy on the test set, demonstrating practical, reliable diagnostic support.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Related work

Proposed hybrid approach

Experiments and results

A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection Ahmed AdilNafea*1, ManarAL-Mahdawi2, Khattab MAli Alheeti3, Mustafa S. Ibrahim Alsumaidaie3, Mohammed MAL-Ani4 1Department of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, Iraq. 2Department of Physics, College of Science, AL-Nahrain University, Baghdad, Iraq. 3Department of Computer Science, University of Anbar Ramadi, Iraq. 4Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia. *Corresponding author. Received 13/09/2023, Revised 05/12/2023, Accepted 07/12/2023, Published Online First 20/03/2024, Published 01/01/2024 © 2022 The Author(s) . Published by College of Science for Women, University of Baghdad. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Breast cancer is a health concern of importance, and it is crucial to detect it early for effective treatment. Recently there has been increasing interest in using artificial intelligence (AI) for breast cancer detection, which has shown results in enhancing accuracy and reducing false positives. However, there are some limitations regarding accuracy in detection. This study introduces an approach that utilizes 1D CNN as feature extraction and employs machine learning (ML) algorithms such as XGBoost, random forests (RF), decision trees (DT) support vector machines (SVM) and k nearest neighbor (KNN) to classify samples as either benign or malignant aiming to enhance accuracy. Our findings reveal that the XGBoost algorithm with feature extraction (1D CNN) achieved an accuracy of 98.24% on the test set. This study highlights the feasibility of employing machine learning algorithms and deep learning (DL) . This study uses a dataset of Wisconsin breast cancer (WBC), for detecting breast cancer. The proposed approach has a good detection and improving outcomes via shows accurate and reliable tools for diagnosing breast cancer. Keywords: Breast cancer diagnosis, Deep learning, Machine learning, Wisconsin, 1D-CNN. Introduction Breast cancer is a significant global health issue, with timely identification and diagnosis playing a key role in enhancing patient prediction. In recent developments in technology, ML and DL have shown a good tools in the fight besides breast cancer. These techniques have shown capable results in the prediction of breast cancer, Assisting healthcare professionals in making well-informed choices regarding patient treatment 1. The ML is a subfield of AI and centers on crafting algorithms efficient of developing knowledge from data. This could be applied to statistical models and algorithms to identify complex relationships and models in large datasets. In the medical domain, ML algorithms have found several employments, covering disease detection and forecasting. In the breast cancer, these algorithms can be learned using large repositories of medical images and patient details, enabling the recognition of breast cancer attributes and potential dangers 2. DL is a type of ML that utilizes artificial neural networks to model complicated relationships between inputs and outputs. Large datasets may be utilized to train DL algorithms, which makes it possible to automatically identify and classify breast cancers this to increases identification accuracy and reduces the need for manual evaluation 3. The core benefit of utilizing DL and ML for breast cancer detection is the ability to deal with large datasets contained from demographic, clinical, and image data based on a number of risk variables, models that correctly evaluation the possibility of detection breast cancer might be created with

FAQs

What hybrid method does the study propose for breast cancer detection?

The study uses 1D CNN for feature extraction and combines it with machine learning classifiers such as XGBoost, random forests, decision trees, SVM, and KNN to classify samples as benign or malignant.

Which dataset is used to train and evaluate the proposed system?

The approach is evaluated using the Wisconsin breast cancer (WBC) dataset.

What performance does the best model achieve?

The XGBoost algorithm with 1D CNN feature extraction achieves 98.24% accuracy on the test set.

Aug 5, 2026

Upload by Ezra