Breast cancer remains a pervasive global health concern, contributing significantly to cancer-related morbidity and mortality among women. Traditional diagnostic methods, such as mammography and clinical breast exams, though valuable, possess limitations, including sensitivity issues and the risk of false positives. In response to these challenges, the emergence of data mining and machine learning technologies has opened new avenues for breast cancer detection. This review examines the application of data mining and machine learning approaches in breast cancer detection and analysis, emphasizing recent advancements and critical findings. The review included an analysis and discussion of the effectiveness of these technologies in improving diagnostic accuracy, the examination of commonly algorithms, and the identification of research gaps. The review highlights the transformative potential of data-driven medical diagnostics, offering valuable insights for researchers, clinicians, and policymakers.

Breast cancer detection, Data mining and machine learning, Algorithm analysis, Medical diagnostic innovation.

Singh M, Dubey AK.Machine learning and data mining for breast cancer detection: a comprehensive review. ACCENTS Transactions on Image Processing and Computer Vision. 2024;10(26):1-6. DOI:10.19101/TIPCV.2023.924003

[1]Ranjbarzadeh R, Dorosti S, Ghoushchi SJ, Caputo A, Tirkolaee EB, Ali SS, et al. Breast tumor localization and segmentation using machine learning techniques: overview of datasets, findings, and methods. Computers in Biology and Medicine. 2023; 152:106443.

[2]Babichev S, Yasinska-Damri L, Liakh I. A hybrid model of cancer diseases diagnosis based on gene expression data with joint use of data mining methods and machine learning techniques. Applied Sciences. 2023; 13(10):6022.

[3]Dubey AK, Gupta U, Jain S. Breast cancer statistics and prediction methodology: a systematic review and analysis. Asian Pacific Journal of Cancer Prevention. 2015; 16(10):4237-45.

[4]Liza FT, Das MC, Pandit PP, Farjana A, Islam AM, Tabassum F. Machine learning-based relative performance analysis for breast cancer prediction. In world AI IoT congress 2023 (pp. 0007-0012). IEEE.

[5]Kiliç AE, Karakoyun M. Breast cancer detection using machine learning algorithms. International Journal of Advanced Natural Sciences and Engineering Researches. 2023; 7(3):91-5.

[6]Akhtar N, Pant H, Dwivedi A, Jain V, Perwej Y. A breast cancer diagnosis framework based on machine learning. International Journal of Scientific Research in Science, Engineering and Technology. 2023:2395-1990.

[7]Dubey AK, Gupta U, Jain S. Analysis of k-means clustering approach on the breast cancer Wisconsin dataset. International Journal of Computer Assisted Radiology and Surgery. 2016; 11:2033-47.

[8]Nemade V, Pathak S, Dubey AK, Barhate D. A review and computational analysis of breast cancer using different machine learning techniques. International Journal of Emerging Technology and Advanced Engineering. 2022; 12(3):111-8.

[9]Kuruba C, Pushpalatha N, Ramu G, Suneetha I, Kumar MR, Harish P. Data mining and deep learning-based hybrid health care application. Applied Nanoscience. 2023; 13(3):2431-7.

[10]Al-Dmour NA, Said RA, Alzoubi HM, Alshurideh M, Ali L. Breast cancer prediction using machine learning and image processing optimization. In the effect of information technology on business and marketing intelligence systems 2023 (pp. 2067-79). Cham: Springer International Publishing.

[11]Ramakrishna MT, Venkatesan VK, Izonin I, Havryliuk M, Bhat CR. Homogeneous adaboost ensemble machine learning algorithms with reduced entropy on balanced data. Entropy. 2023; 25(2):245.

[12]Wu R, Luo J, Wan H, Zhang H, Yuan Y, Hu H, et al. Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, epidemiology, and end results database. Plos One. 2023; 18(1):e0280340.

[13]Nemade V, Pathak S, Dubey AK. Deep learning-based ensemble model for classification of breast cancer. Microsystem Technologies. 2023:1-5.

[14]Sharma A, Hooda N, Gupta NR, Sharma R. Efficient RIEV: a novel framework for the prediction of breast cancer cases using ensemble machine learning. Network Modeling Analysis in Health Informatics and Bioinformatics. 2023; 12(1):29.

[15]Dubey A, Gupta U, Jain S. Medical data clustering and classification using TLBO and machine learning algorithms. Computers, Materials and Continua. 2021; 70(3):4523-43.

[16]Avcı H, Karakaya J. A novel medical image enhancement algorithm for breast cancer detection on mammography images using machine learning. Diagnostics. 2023; 13(3):348.

[17]Liu J, Lei J, Ou Y, Zhao Y, Tuo X, Zhang B, et al. Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis. Clinical and Experimental Medicine. 2023; 23(6):2341-56.

[18]Sun X, Qourbani A. Combining ensemble classification and integrated filter-evolutionary search for breast cancer diagnosis. Journal of Cancer Research and Clinical Oncology. 2023:1-7.

[19]Rashed AE, Elmorsy AM, Atwa AE. Comparative evaluation of automated machine learning techniques for breast cancer diagnosis. Biomedical Signal Processing and Control. 2023; 86:105016.

[20]Prajapati JB, Paliwal H, Prajapati BG, Saikia S, Pandey R. Quantum machine learning in prediction of breast cancer. In quantum computing: a shift from bits to qubits 2023 (pp. 351-82). Singapore: Springer Nature Singapore.

[21]Rovshenov A, Peker S. Performance comparison of different machine learning techniques for early prediction of breast cancer using Wisconsin breast cancer dataset. In 3rd international informatics and software engineering conference (IISEC) 2022 (pp. 1-6). IEEE.

[22]Jiang D, Zhao J, Zhang Y, Cong B, Shen Y, Gao F, et al. Integrated photoacoustic pen for breast cancer sentinel lymph node detection. In international ultrasonics symposium 2022 (pp. 1-3). IEEE.

[23]Basha HM, Sindhu G. Improved accuracy of early stage breast cancer detection using anisotropic diffusion algorithm and Variational partial differential equation method. In international conference on sustainable computing and data communication systems 2022 (pp. 1683-9). IEEE.

[24]Nelli S. Prediction of Early Stage Breast Cancer by Injection of Gold Nano Particles and Analyzing Images using Data Analytics. In 2nd international conference on mobile networks and wireless communications 2022 (pp. 1-5). IEEE.

[25]Dubey C, Shukla N, Kumar D, Singh AK, Dwivedi VK. Breast cancer modeling and prediction combining machine learning and artificial neural network approaches. In international conference on computing, communication, and intelligent systems 2022 (pp. 119-24). IEEE.

[26]Botlagunta M, Botlagunta MD, Myneni MB, Lakshmi D, Nayyar A, Gullapalli JS, et al. Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms. Scientific Reports. 2023; 13(1):485.

[27]Nemade V, Fegade V. Machine learning techniques for breast cancer prediction. Procedia Computer Science. 2023; 218:1314-20.

[28]Sugimoto M, Hikichi S, Takada M, Toi M. Machine learning techniques for breast cancer diagnosis and treatment: a narrative review. Annals of Breast Surgery. 2023; 7.

[29]Elsadig MA, Altigani A, Elshoush HT. Breast cancer detection using machine learning approaches: a comparative study. International Journal of Electrical & Computer Engineering (2088-8708). 2023; 13(1).

[30]Manikandan P, Durga U, Ponnuraja C. An integrative machine learning framework for classifying SEER breast cancer. Scientific Reports. 2023; 13(1):5362.

[31]Ebrahim M, Sedky AA, Mesbah S. Accuracy assessment of machine learning algorithms used to predict breast cancer. Data. 2023; 8(2):35.