Skip to content
2000
Volume 18, Issue 3
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

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.

Methods

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.

Results

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.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/rascs/10.2174/0126662558297605240926055723
2024-10-11
2025-07-15

  • 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. KhanW. A. ArunimaP.D. A Combined PCA-MLP Network for Early Breast Cancer Detection.arXiv2021
     [Google Scholar]
  2. SinghV.K. RashwanH.A. RomaniS. AkramF. PandeyN. SarkerM.M.K. SalehA. ArenasM. ArquezM. PuigD. Torrents-BarrenaJ. Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network.Expert Syst. Appl.202013911285510.1016/j.eswa.2019.112855
     [Google Scholar]
  3. MaheshV. An ensemble classification based approach for breast cancer prediction.IOP Conf.: Materials Science and Engineering2021110
     [Google Scholar]
  4. NassifA.B. TalibM.A. NasirQ. AfadarY. ElgendyO. Breast cancer detection using artificial intelligence techniques: A systematic literature review.Artif. Intell. Med.202212710227610.1016/j.artmed.2022.10227635430037
     [Google Scholar]
  5. SharmaD. KumarR. JainA. A Systematic Review of Risk Factors and Risk Assessment Models for Breast Cancer.Mobile Radio Communications and 5G Networks202150951910.1007/978‑981‑15‑7130‑5_41
     [Google Scholar]
  6. VyasS. ChauhanA. RanaD. AnsariN. Breast Cancer Detection Using Machine Learning Techniques.Int. J. Res. Appl. Sci. Eng. Technol.20221053232323710.22214/ijraset.2022.43055
     [Google Scholar]
  7. BrayF. FerlayJ. SoerjomataramI. SiegelR.L. TorreL.A. JemalA. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.201868639442410.3322/caac.2149230207593
     [Google Scholar]
  8. KhanS. IslamN. JanZ. Ud DinI. RodriguesJ.J.P.C. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning.Pattern Recognit. Lett.20191251610.1016/j.patrec.2019.03.022
     [Google Scholar]
  9. AmrH. Application of Rough Sets to Predict the Breast Cancer Risk Association with Routine Blood Analyses.IJITEE202110
     [Google Scholar]
  10. MasoodH. Breast Cancer Detection using Machine Learning Algorithm.IRJET202108
     [Google Scholar]
  11. MangukiyaM. VaghaniA. SavaniM. Breast Cancer Detection with Machine Learning.IJTASET202210141145
     [Google Scholar]
  12. PooraniS. BalasubramanieP. Deep Neural Network Classifier in Breast Cancer Prediction.Int. J. Eng. Adv. Technol.2019912106210910.35940/ijeat.A9664.109119
     [Google Scholar]
  13. GuptaC. SharmaK. Early Breast Cancer Detection using Various Machine Learning Techniques.IJERT20221106114121
     [Google Scholar]
  14. PaulS. SolankiP.P. ShahiU.P. SrikrishnaS. 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.201516188163817110.7314/APJCP.2015.16.18.816326745055
     [Google Scholar]
  15. RajendranK. JayabalanM. SivakumarV. Feasibility Study on Data Mining Techniques in Diagnosis of Breast Cancer.IJMLC201993328333
     [Google Scholar]
  16. RasoolA. BunterngchitC. TiejianL. IslamM.R. QuQ. JiangQ. Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis.Int. J. Environ. Res. Public Health2022196321110.3390/ijerph1906321135328897
     [Google Scholar]
  17. LiY. ChenZ. Performance Evaluation of Machine Learning Methods for Breast Cancer Prediction.Appl. Comput. Math.20187421221610.11648/j.acm.20180704.15
     [Google Scholar]
  18. AlfianG. SyafrudinM. Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method.Computers20221113610.3390/computers11090136
     [Google Scholar]
  19. AamirS. RahimA. AamirZ. AbbasiS.F. KhanM.S. AlhaisoniM. KhanM.A. KhanK. AhmadJ. Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques.Comput. Math. Methods Med.2022202211310.1155/2022/586952936017156
     [Google Scholar]
  20. PatrícioM. PereiraJ. CrisóstomoJ. MatafomeP. GomesM. SeiçaR. CarameloF. Using Resistin, glucose, age and BMI to predict the presence of breast cancer.BMC Cancer20181812910.1186/s12885‑017‑3877‑129301500
     [Google Scholar]
  21. AnupamS. Data Mining and Principal Component Analysis on Coimbra Breast Cancer Dataset.AIJR Proceedings20214249
     [Google Scholar]
  22. QianB.Z. LiJ. ZhangH. KitamuraT. ZhangJ. CampionL.R. KaiserE.A. SnyderL.A. PollardJ.W. CCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis.Nature2011475735522222510.1038/nature1013821654748
     [Google Scholar]
  23. RaviyaK.H. GajjarB. Performance Evaluation of different data mining classification algorithm using WEKA.Indian J. Res20131921
     [Google Scholar]
  24. OthmanB. YauM.F. Comparison of Different Classification Techniques Using WEKA for breast cancerInternational Conference on Biomedical Engineering2006
     [Google Scholar]
  25. AltmanN.S. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression.The American Statistician199246317518510.1080/00031305.1992.10475879
     [Google Scholar]
  26. GhoshS. RoyS. A tutorial review on Text Mining Algorithms.Int. J. Adv. Res. Comput. Commun. Eng.201217
     [Google Scholar]
  27. HosseinM. Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure.Appl. Sci20199
     [Google Scholar]
  28. AnsariM.A. SinghD.K. A Review of Machine Learning Approaches for Human Detection through Feature Based Classification.Int. J. Comp. Dig. Sys.202212356958610.12785/ijcds/120146
     [Google Scholar]
  29. AnsariM.A. SinghD.K. Human detection techniques for real time surveillance: a comprehensive survey.Multimedia Tools Appl.20218068759880810.1007/s11042‑020‑10103‑4
     [Google Scholar]
  30. KumarS. ChaubeM.K. AlsamhiS.H. GuptaS.K. GuizaniM. GravinaR. FortinoG. 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.202222610710910.1016/j.cmpb.2022.10710936174422
     [Google Scholar]
  31. KumarS. BhagatV. SahuP. ChaubeM.K. BeheraA.K. GuizaniM. GravinaR. Di DioM. FortinoG. CurryE. AlsamhiS.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.202424310791110.1016/j.cmpb.2023.10791137981453
     [Google Scholar]
  32. MawloodD.F. Enhanced machine learning models for predicting breast cancer: Healthcare system.ITM Web Conf 2nd International Conference on Applied Computing & Smart Cities (ICACS24)202410.1051/itmconf/20246401020
     [Google Scholar]
  33. Ala’aR. Neural Network Approaches for Early Breast Cancer Detection.ESI Preprints2024
     [Google Scholar]
  34. SalodZakia SinghYashik Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol.J Public Health Res.201983167710.4081/jphr.2019.1677
     [Google Scholar]
  35. SrinivasTaarun Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer.Mathematical Problems in Engineering2022
     [Google Scholar]
  36. AlsabryA. AlgabriM. AhsanA.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 Technology202312210.59628/jast.v1i2.361
     [Google Scholar]
  37. RaiH.M. YooJ. A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics.J. Cancer Res. Clin. Oncol.202314915143651440810.1007/s00432‑023‑05216‑w37540254
     [Google Scholar]
  38. CalistoF.M. SantiagoC. NunesN. NascimentoJ.C. Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification.Int. J. Hum. Comput. Stud.202115010260710.1016/j.ijhcs.2021.102607
     [Google Scholar]
  39. F.G.T. Cova, J.B. Daniel, C.C. Sandra, Computational approaches in theranostics: mining and predicting cancer data.Pharmaceutics2019
     [Google Scholar]
  40. El MirI. El KafhaliS. The state of the art of using artificial intelligence for disease identification and diagnosis in healthcare.Deep Learning for Healthcare Decision Making20234776
     [Google Scholar]
  41. BraikM. AwadallahM.A. Al-BetarM.A. HammouriA.I. AlzubiO.A. Cognitively enhanced versions of capuchin search algorithm for feature selection in medical diagnosis: A COVID-19 case study.Cognit. Comput.20231561884192110.1007/s12559‑023‑10149‑037362196
     [Google Scholar]
  42. AnsariMohd SinghDushyant Kumar Significance of Color Spaces and Their Selection for Image Processing: A Survey.Rec. Adv. Com. Sci. Comm. (Formerly: Rec. Patent. Comp. Sci.),202215710.2174/2666255814666210308152108
     [Google Scholar]
  43. BommuR. Advancements in Healthcare Information Technology: A Comprehensive Review.Innovative Computer Sciences Journal20228117
     [Google Scholar]
  44. KurchaniyaDiksha AnsariMohd PatelDurga An Enhanced Approach for Number Plate Detection and Recognition.Rec. Adv. Com. Sci. Comm. (Formerly: Rec. Patent. Comp. Sci.),2022153412420
     [Google Scholar]
  45. Riazul IslamS.M. Daehan Kwak Humaun KabirM. HossainM. Kyung-Sup Kwak The internet of things for health care: a comprehensive survey.IEEE Access2015367870810.1109/ACCESS.2015.2437951
     [Google Scholar]
  46. LewandrowskiC. Artificial intelligence approach in melanoma.Melanoma2019131
     [Google Scholar]
  47. LopesN. Machine Learning for Adaptive Multi-Core Machines. Diss2014
     [Google Scholar]
  48. MehmoodM. Machine learning enabled early detection of breast cancer by structural analysis of mammograms.Comput. Mater. Continua202167164165710.32604/cmc.2021.013774
     [Google Scholar]
  49. OmotehinwaT.O. OyewolaD.O. DadaE.G. A light gradient-boosting machine algorithm with tree-structured parzen estimator for breast cancer diagnosis.Healthc. Anal.2023410021810.1016/j.health.2023.100218
     [Google Scholar]
  50. JaiswalV. SaurabhP. LilhoreU.K. PathakM. SimaiyaS. DalalS. A breast cancer risk predication and classification model with ensemble learning and big data fusion.Decision Ana. J.2023810029810.1016/j.dajour.2023.100298
     [Google Scholar]
/content/journals/rascs/10.2174/0126662558297605240926055723
Loading
Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset Analysis
Recent Advances in Computer Science and Communications 18, E26662558297605 (2025); 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
Keyword(s): artificial neural network; classification algorithms; early cancer diagnosis; feature extraction; Healthcare analysis; preprocessing

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