Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Recent Advances in Computer Science and Communications
  4. Fast Track Articles
  5. Fast Track Article
image of Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset Analysis

Abstract

Aim

The aim of this work is to succinctly communicate the key aspects of a research study on breast cancer. This includes highlighting the global impact and prevalence of breast cancer, emphasizing the challenges of early diagnosis, discussing the potential of technological advancements, and showcasing the role of machine learning algorithms in the context of liver cancer diagnosis.

Background

Cancer, notably breast cancer, represents a global health challenge, claiming a significant toll with 12.5% of new cancer cases annually. The prevalence of breast cancer among women worldwide is alarming, resulting in 2.26 million incidents and the unfortunate loss of 685,000 lives.

Objective

This article focuses on the critical aspect of early breast cancer diagnosis, acknowledging its heightened difficulty in developing nations compared to developed counterparts. The potential for advancements in technology to serve as a beacon of hope lies in early identification and timely treatment, offering salvation to numerous women and significantly elevating survival chances.

Method

In this intricate landscape, machine learning algorithms, particularly in diagnosing liver cancer at its nascent stages, emerge as instrumental tools. The study employs the latest Coimbra dataset, encompassing nine key attributes and a binary classification attribute, with values 1 and 2 signifying benign and malignant cases, respectively.

Result

Supervised machine learning algorithms, including Bayes net, multilayer perceptron, IBK, random committee, and random tree, are meticulously applied. Certain models exhibit superior accuracy, precision, recall, and performance, positioning them as promising cornerstones for breast cancer analysis.

Conclusion

This structured abstract highlights the urgent need for effective screening and prevention strategies, emphasizing the potential of advanced technology and machine learning algorithms to play a pivotal role in the early detection and analysis of breast cancer, offering hope for improved outcomes and survival rates.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/rascs/10.2174/0126662558297605240926055723
2024-10-11
2024-11-29

  • From This Site

    /content/journals/rascs/10.2174/0126662558297605240926055723
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Khan W. A. Arunima P.D. A Combined PCA-MLP Network for Early Breast Cancer Detection. arXiv 2021
    [Google Scholar]
  2. Singh V.K. Rashwan H.A. Romani S. Akram F. Pandey N. Sarker M.M.K. Saleh A. Arenas M. Arquez M. Puig D. Torrents-Barrena J. Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst. Appl. 2020 139 112855 10.1016/j.eswa.2019.112855
    [Google Scholar]
  3. Mahesh V. An ensemble classification based approach for breast cancer prediction. IOP Conf.: Materials Science and Engineering 2021 1 10
    [Google Scholar]
  4. Nassif A.B. Talib M.A. Nasir Q. Afadar Y. Elgendy O. Breast cancer detection using artificial intelligence techniques: A systematic literature review. Artif. Intell. Med. 2022 127 102276 10.1016/j.artmed.2022.102276 35430037
    [Google Scholar]
  5. Sharma D. Kumar R. Jain A. A Systematic Review of Risk Factors and Risk Assessment Models for Breast Cancer. Mobile Radio Communications and 5G Networks 2021 509 519 10.1007/978‑981‑15‑7130‑5_41
    [Google Scholar]
  6. Vyas S. Chauhan A. Rana D. Ansari N. Breast Cancer Detection Using Machine Learning Techniques. Int. J. Res. Appl. Sci. Eng. Technol. 2022 10 5 3232 3237 10.22214/ijraset.2022.43055
    [Google Scholar]
  7. Bray F. Ferlay J. Soerjomataram I. Siegel R.L. Torre L.A. Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018 68 6 394 424 10.3322/caac.21492 30207593
    [Google Scholar]
  8. Khan S. Islam N. Jan Z. Ud Din I. Rodrigues J.J.P.C. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit. Lett. 2019 125 1 6 10.1016/j.patrec.2019.03.022
    [Google Scholar]
  9. Amr H. Application of Rough Sets to Predict the Breast Cancer Risk Association with Routine Blood Analyses. IJITEE 2021 10
    [Google Scholar]
  10. Masood H. Breast Cancer Detection using Machine Learning Algorithm. IRJET 2021 08
    [Google Scholar]
  11. Mangukiya M. Vaghani A. Savani M. Breast Cancer Detection with Machine Learning. IJTASET 2022 10 141 145
    [Google Scholar]
  12. Poorani S. Balasubramanie P. Deep Neural Network Classifier in Breast Cancer Prediction. Int. J. Eng. Adv. Technol. 2019 9 1 2106 2109 10.35940/ijeat.A9664.109119
    [Google Scholar]
  13. Gupta C. Sharma K. Early Breast Cancer Detection using Various Machine Learning Techniques. IJERT 2022 11 06 114 121
    [Google Scholar]
  14. Paul S. Solanki P.P. Shahi U.P. Srikrishna S. Epidemiological Study on Breast Cancer Associated Risk Factors and Screening Practices among Women in the Holy City of Varanasi, Uttar Pradesh, India. Asian Pac. J. Cancer Prev. 2015 16 18 8163 8171 10.7314/APJCP.2015.16.18.8163 26745055
    [Google Scholar]
  15. Rajendran K. Jayabalan M. Sivakumar V. Feasibility Study on Data Mining Techniques in Diagnosis of Breast Cancer. IJMLC 2019 9 3 328 333
    [Google Scholar]
  16. Rasool A. Bunterngchit C. Tiejian L. Islam M.R. Qu Q. Jiang Q. Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis. Int. J. Environ. Res. Public Health 2022 19 6 3211 10.3390/ijerph19063211 35328897
    [Google Scholar]
  17. Li Y. Chen Z. Performance Evaluation of Machine Learning Methods for Breast Cancer Prediction. Appl. Comput. Math. 2018 7 4 212 216 10.11648/j.acm.20180704.15
    [Google Scholar]
  18. Alfian G. Syafrudin M. Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method. Computers 2022 11 136 10.3390/computers11090136
    [Google Scholar]
  19. Aamir S. Rahim A. Aamir Z. Abbasi S.F. Khan M.S. Alhaisoni M. Khan M.A. Khan K. Ahmad J. Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques. Comput. Math. Methods Med. 2022 2022 1 13 10.1155/2022/5869529 36017156
    [Google Scholar]
  20. Patrício M. Pereira J. Crisóstomo J. Matafome P. Gomes M. Seiça R. Caramelo F. Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer 2018 18 1 29 10.1186/s12885‑017‑3877‑1 29301500
    [Google Scholar]
  21. Anupam S. Data Mining and Principal Component Analysis on Coimbra Breast Cancer Dataset. AIJR Proceedings 2021 42 49
    [Google Scholar]
  22. Qian B.Z. Li J. Zhang H. Kitamura T. Zhang J. Campion L.R. Kaiser E.A. Snyder L.A. Pollard J.W. CCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis. Nature 2011 475 7355 222 225 10.1038/nature10138 21654748
    [Google Scholar]
  23. Raviya K.H. Gajjar B. Performance Evaluation of different data mining classification algorithm using WEKA. Indian J. Res 2013 19 21
    [Google Scholar]
  24. Othman B. Yau M.F. Comparison of Different Classification Techniques Using WEKA for breast cancer International Conference on Biomedical Engineering 2006
    [Google Scholar]
  25. Altman N.S. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. The American Statistician 1992 46 3 175 185 10.1080/00031305.1992.10475879
    [Google Scholar]
  26. Ghosh S. Roy S. A tutorial review on Text Mining Algorithms. Int. J. Adv. Res. Comput. Commun. Eng. 2012 1 7
    [Google Scholar]
  27. Hossein M. Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure. Appl. Sci 2019 9
    [Google Scholar]
  28. Ansari M.A. Singh D.K. A Review of Machine Learning Approaches for Human Detection through Feature Based Classification. Int. J. Comp. Dig. Sys. 2022 12 3 569 586 10.12785/ijcds/120146
    [Google Scholar]
  29. Ansari M.A. Singh D.K. Human detection techniques for real time surveillance: a comprehensive survey. Multimedia Tools Appl. 2021 80 6 8759 8808 10.1007/s11042‑020‑10103‑4
    [Google Scholar]
  30. Kumar S. Chaube M.K. Alsamhi S.H. Gupta S.K. Guizani M. Gravina R. Fortino G. A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques. Comput. Methods Programs Biomed. 2022 226 107109 10.1016/j.cmpb.2022.107109 36174422
    [Google Scholar]
  31. Kumar S. Bhagat V. Sahu P. Chaube M.K. Behera A.K. Guizani M. Gravina R. Di Dio M. Fortino G. Curry E. Alsamhi S.H. A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases. Comput. Methods Programs Biomed. 2024 243 107911 10.1016/j.cmpb.2023.107911 37981453
    [Google Scholar]
  32. Mawlood D.F. Enhanced machine learning models for predicting breast cancer: Healthcare system. ITM Web Conf 2nd International Conference on Applied Computing & Smart Cities (ICACS24) 2024 10.1051/itmconf/20246401020
    [Google Scholar]
  33. Ala’a R. Neural Network Approaches for Early Breast Cancer Detection. ESI Preprints 2024
    [Google Scholar]
  34. Salod Zakia Singh Yashik Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol. J Public Health Res. 2019 8 3 1677 10.4081/jphr.2019.1677
    [Google Scholar]
  35. Srinivas Taarun Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer. Mathematical Problems in Engineering 2022
    [Google Scholar]
  36. Alsabry A. Algabri M. Ahsan A.M. Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature. Sana'a University Journal of Applied Sciences and Technology 2023 1 2 2 10.59628/jast.v1i2.361
    [Google Scholar]
  37. Rai H.M. Yoo J. A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics. J. Cancer Res. Clin. Oncol. 2023 149 15 14365 14408 10.1007/s00432‑023‑05216‑w 37540254
    [Google Scholar]
  38. Calisto F.M. Santiago C. Nunes N. Nascimento J.C. Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification. Int. J. Hum. Comput. Stud. 2021 150 102607 10.1016/j.ijhcs.2021.102607
    [Google Scholar]
  39. Computational approaches in theranostics: mining and predicting cancer data. Pharmaceutics 2019
    [Google Scholar]
  40. El Mir I. El Kafhali S. The state of the art of using artificial intelligence for disease identification and diagnosis in healthcare. Deep Learning for Healthcare Decision Making 2023 47 76
    [Google Scholar]
  41. Braik M. Awadallah M.A. Al-Betar M.A. Hammouri A.I. Alzubi O.A. Cognitively enhanced versions of capuchin search algorithm for feature selection in medical diagnosis: A COVID-19 case study. Cognit. Comput. 2023 15 6 1884 1921 10.1007/s12559‑023‑10149‑0 37362196
    [Google Scholar]
  42. Ansari Mohd Singh Dushyant Kumar Significance of Color Spaces and Their Selection for Image Processing: A Survey. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) 2022 15 7 10.2174/2666255814666210308152108
    [Google Scholar]
  43. Bommu R. Advancements in Healthcare Information Technology: A Comprehensive Review. Innovative Computer Sciences Journal 2022 8 1 1 7
    [Google Scholar]
  44. Kurchaniya Diksha Ansari Mohd Patel Durga An Enhanced Approach for Number Plate Detection and Recognition. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) 2022 15 3 412 420
    [Google Scholar]
  45. Riazul Islam S.M. Daehan Kwak Humaun Kabir M. Hossain M. Kyung-Sup Kwak The internet of things for health care: a comprehensive survey. IEEE Access 2015 3 678 708 10.1109/ACCESS.2015.2437951
    [Google Scholar]
  46. Lewandrowski C. Artificial intelligence approach in melanoma. Melanoma 2019 1 31
    [Google Scholar]
  47. Lopes N. Machine Learning for Adaptive Multi-Core Machines. 2014
    [Google Scholar]
  48. Mehmood M. Machine learning enabled early detection of breast cancer by structural analysis of mammograms. Comput. Mater. Continua 2021 67 1 641 657 10.32604/cmc.2021.013774
    [Google Scholar]
  49. Omotehinwa T.O. Oyewola D.O. Dada E.G. A light gradient-boosting machine algorithm with tree-structured parzen estimator for breast cancer diagnosis. Healthc. Anal. 2023 4 100218 10.1016/j.health.2023.100218
    [Google Scholar]
  50. Jaiswal V. Saurabh P. Lilhore U.K. Pathak M. Simaiya S. Dalal S. A breast cancer risk predication and classification model with ensemble learning and big data fusion. Decision Analytics Journal 2023 8 100298 10.1016/j.dajour.2023.100298
    [Google Scholar]
/content/journals/rascs/10.2174/0126662558297605240926055723
Loading
Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset Analysis
Bentham Science Publishers ; https://doi.org/10.2174/0126662558297605240926055723
/content/journals/rascs/10.2174/0126662558297605240926055723
/content/journals/rascs/10.2174/0126662558297605240926055723
Loading

Data & Media loading...


  • Article Type:     Research Article
Keywords: Artificial Neural Network ; Preprocessing ; Classification Algorithms ; Healthcare Analysis ; Early cancer diagnosis ; Feature Extraction

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test