A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection
DOI:
https://doi.org/10.21123/bsj.2024.9443Keywords:
Breast cancer diagnosis, Deep learning, Machine learning, Wisconsin, 1D-CNNAbstract
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.
Received 13/09/2023
Revised 05/12/2023
Accepted 07/12/2023
Published Online First 20/03/2024
References
Waks AG, Winer EP. Breast cancer treatment: a review. Jama. 2019; 321(3): 288–300. https://doi.org/10.1001/jama.2018.19323
Averbuch T, Sullivan K, Sauer A, Mamas MA, Voors AA, Gale CP, et al. Applications of artificial intelligence and machine learning in heart failure. Eur hear J health. 2022; 3(2): 311–22. https://doi.org/10.1093/ehjdh/ztac025
Chen K, Zhai X, Wang S, Li X, Lu Z, Xia D, et al. Emerging trends and research foci of deep learning in spine: bibliometric and visualization study. Neurosurg Rev. 2023; 46(1): 81. https://doi.org/10.1007/s10143-023-01987-5
Arshad MW. Prediction and diagnosis of breast cancer using machine learning and ensemble classifiers. Cent Asian J Math Theory Comput Sci. 2023; 4(1): 49–56. https://doi.org/10.17605/OSF.IO/9CFN6
Teixeira F, Montenegro JLZ, da Costa CA, da Rosa Righi R. An analysis of machine learning classifiers in breast cancer diagnosis. In: 2019 XLV Latin American computing conference (CLEI). IEEE; 2019: 1–10. https://doi.org/10.1109/CLEI47609.2019.235094
Elsadig MA, Altigani A, Elshoush HT. Breast cancer detection using machine learning approaches: a comparative study. Int J Electr Comput Eng. 2023; 13(1): 736-45. https://doi.org/10.11591/ijece.v13i1.pp736-745
Chen H, Wang N, Du X, Mei K, Zhou Y, Cai G. Classification prediction of breast cancer based on machine learning. Comput Intell Neurosci. 2023; 2023. https://doi.org/10.1155/2023/6530719
Sakib S, Yasmin N, Tanzeem AK, Shorna F, Hasib K MD, Alam SB. Breast cancer detection and classification: A comparative analysis using machine learning algorithms. In: Proceedings of Third International Conference on Communication, Computing and Electronics Systems: ICCCES 2021. Springer; 2022: 703–17. https://doi.org/10.1007/978-981-16-8862-1_46
Abiodun MK, Misra S, Awotunde JB, Adewole S, Joshua A, Oluranti J. Comparing the performance of various supervised machine learning techniques for early detection of breast cancer. In: International Conference on Hybrid Intelligent Systems. Springer; 2021: 473–82. https://doi.org/10.1007/978-3-030-96305-7_44
Sridevi T, Murugan A. An intelligent classifier for breast cancer diagnosis based on K-Means clustering and rough set. Int J Comput Appl. 2014; 85(11). https://doi.org/10.5120/14889-3336
Henderi H, Wahyuningsih T, Rahwanto E. Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. Int J Informatics Inf Syst. 2021; 4(1): 13–20. https://doi.org/10.47738/ijiis.v4i1.73
Kareem AK, Al-ani MM, Nafea AA. Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network. Baghdad Sci J. 2023; 20: 1182–93. https://doi.org/10.21123/bsj.2023.8564
Alajanbi M, Malerba D, Liu H. Distributed reduced convolution neural networks. Mesopotamian J Big Data. 2021; 2021: 26–9. https://doi.org/10.58496/MJBD/2021/005
Dabiri H, Farhangi V, Moradi MJ, Zadehmohamad M, Karakouzian M. Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars. Appl Sci. 2022; 12(10): 4851. https://doi.org/10.3390/app12104851
Charbuty B, Abdulazeez A. Classification based on decision tree algorithm for machine learning. J Appl Sci Technol Trends. 2021; 2(01): 20–8. https://doi.org/10.38094/jastt20165
Chandrahas NS, Choudhary BS, Teja MV, Venkataramayya MS, Prasad NSRK. XG boost algorithm to simultaneous prediction of rock fragmentation and induced ground vibration using unique blast data. Appl Sci. 2022; 12(10): 5269. https://doi.org/10.3390/app12105269
Roy A, Chakraborty S. Support vector machine in structural reliability analysis: A review. Reliab Eng Syst Saf. 2023; 233:109126. https://doi.org/10.1016/j.ress.2023.109126
Kurani A, Doshi P, Vakharia A, Shah M. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Ann Data Sci. 2023;10(1):183–208. https://doi.org/10.1007/s40745-021-00344-x
Lubis AR, Lubis M. Optimization of distance formula in K-Nearest Neighbor method. Bull Electr Eng Informatics. 2020; 9(1) :326–38. https://doi.org/10.11591/eei.v9i1.1464
ElSahly O, Abdelfatah A. An incident detection model using random forest classifier. Smart Cities.2023;6(4): 1786–813.https://doi.org/10.3390/smartcities6040083
Mukhlif AA, Al-Khateeb B, Mohammed M. Classification of breast cancer images using new transfer learning techniques. Iraqi J Comput Sci Math. 2023; 4(1): 167–80. https://doi.org/10.52866/ijcsm.2023.01.01.0014
Nafea AA, Omar N, Al-qfail ZM. Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection. Baghdad Sci J. 2024; 21(1), pp.0226-0233. https://doi.org/10.21123/bsj.2023.7988
Li JP, Haq AU, Din SU, Khan J, Khan A, Saboor A. Heart disease identification method using machine learning classification in e-healthcare. IEEE access. 2020; 8 :107562–82. https://doi.org/10.1109/ACCESS.2020.3001149
Trivedi NK, Gautam V, Anand A, Aljahdali HM, Villar SG, Anand D, et al. Early detection and classification of tomato leaf disease using high-performance deep neural network. Sensors. 2021;21(23):7987. https://doi.org/10.3390/s21237987
Nafea AA, Mishlish M, Muwafaq A, Shaban S, Al-ani MM, Alheeti KMA, et al. Enhancing Student ’ s Performance Classification Using Ensemble Modeling. Iraqi J Comput Sci Math. 2023; 4(4): 204–14.https://doi.org/10.52866/%20ijcsm.2023.04.04.016
Downloads
Issue
Section
License
Copyright (c) 2024 Ahmed Adil Nafea, Manar AL-Mahdawi, Khattab M Ali Alheeti, Mustafa S. Ibrahim Alsumaidaie, Mohammed M AL-Ani
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
