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Karnauph classifier for predicting breast cancer based on morphological features

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Abstract

Breast cancer is the primary cause of mortality in women in the world, using artificial intelligence in predicting, detecting and early diagnosing of breast cancer can reduce the mortality rate. In this study, we propose a new approach to find the best morphological features that give the best prediction accuracy about the type of cancer benign or malignant. Our proposed algorithm Karnauph algorithm is a hybrid mathematical model that combines between features from the supervised learning and some other features from the unsupervised learning to obtain an easy and robust algorithm in term of prediction and computation. We apply our proposed algorithm on breast cancer dataset and we compare its performance in term of prediction accuracy with that of Naive Bayes, decision tree and Adaboost algorithms. The results show that our algorithm performance is similar to all of these algorithms when they are implemented on the same dataset and it gives better performance than other algorithms when evaluated on unseen data.

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References

  1. Breast Cancer Facts and Statistics: BREASTCANCER.org. https://www.breastcancer.org/facts-statistics. (2022). EIN is 23–3082851 last updated 14/7/2022. 8:23 PM

  2. Hunter B, Hindocha S, Lee RW (2022) The role of artificial intelligence in early cancer diagnosis. MDPI .Cancers 2022, 14, 1524. https://doi.org/10.3390/cancers 14061524. Available on line at: https://www.mdpi.com/Journal/ cancers

  3. Hareendran Chandra (2014) Artificial Intelligence and Machine Learning. Phi, 1st edition. ISBN: 8120349342

  4. Zabian A, Ibrahim AZ (2022) Hybrid mathematical model for data classification and prediction: case study COVID 19. Int J Math Computer Sci 17(3) (ISSN 1814–0432)

  5. Kaggle.com/breast cancer dataset analysis

  6. Rajbongshi N, Bora K, Nath DC, Das AK, Mahanta LB (2018) Analysis of morphological features of benign and malignant breast cell extracted from FNAC microscopic image using pearsonian system of curves. J Cytol 2:99–104. https://doi.org/10.4103/JOC.JOC_198_16

    Article  Google Scholar 

  7. Elderton WP, Johnson NL (2010) System of frequency curves. Chapter. 4, Cambridge University Press. 10/.1017/CB09780511569654. ISBN: 978051156954

  8. Helwan A, Abiyeu R (2016) Shape and texture features for the identification of breast cancer. Proceeding of World Congress on Engineering and Computer Science. 2: 19–21. San Francisco, USA

  9. Al-Azzam N, Shatnawi I (2021) Comparing supervised and semi supervised machine learning models on diagnosing breast cancer. Ann Med Surg. https://doi.org/10.1016/j.asmu.2020.12.043

    Article  Google Scholar 

  10. Rakha EA, Alsaleem M, Elsharawy KA, Toss MS, Raafat S et al (2020) Visual histological assessment of morphological features reflects the underlying molecular profile in invasive breast cancer. A morphomolecular study. Histopathology 77:631–645. https://doi.org/10.1111/his.14199

    Article  Google Scholar 

  11. Naji MA, El Filali S, et al (2021) Machine learning algorithms for breast cancer prediction and diagnosis. International Workshop on Edge IA-IOT for Smart Agriculture (SA2IOT) August 9-12/2021. Leuven-Belgium Published in Science Direct Procedia Computer Science 191:487–492. https://doi.org/10.1016/j.prcs2021.07.062

  12. Thirumalaikolundusubramanian P (2018) Comparison of bayes classifiers for breast cancer classification. Asian Pacific J Cancer Prevent 19(10):2917–920. https://doi.org/10.22034/APJCP.2018.19.10.2917

  13. Kharya S, Agrawal S, Soni S (2014) Naïve bayes classifiers: a probabilistic detection model for breast cancer. Int J Comput Appl. 92(10):26–31

    Google Scholar 

  14. Xong Y, Ye M, Wu C (2021) Cancer classification with a cast sensitive naïve bayes stacking ensemble. Comput Math Methods Med. https://doi.org/10.1155/5556992

    Article  Google Scholar 

  15. Mangukiya M, Vaghani A, Savani M (2022) Breast cancer detection with machine learning. Int J Res Appl Sci Eng Technol (IJRASET) 10:141-145 https://doi.org/10.22214/ijraset.2022.40204

  16. Nathan N, Kabari L, Francis A (2019) Prediction of breast cancer disease using decision tree algorithm. Int J Innovat Inform Syst Technol Res 7(1):34–38

    Google Scholar 

  17. Breast Cancer Wisconsin (Diagnostic). https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic.

  18. Zhang Z, Chen L, Humphries B et al (2018) Morphology based prediction of cancer cell migration using an artificial neural network and random decision forest. Integrat Biol 10:758–767. https://doi.org/10.1039/c8ib00106e

    Article  Google Scholar 

  19. Zabian A (2023) Karnauph classifier: a hybrid mathematical model for data classification. Appl Math Nonlinear Sci. https://doi.org/10.2478/amns.2023.2.00414

    Article  Google Scholar 

  20. Mondal S, Ghosh S, Nag A (2023) Brain stroke prediction model based on boosting and stacking ensemble approach. Int J Inform Technol. https://link.springer.com/article/https://doi.org/10.1007/s41870-023-01418-0

  21. Kakhandaki N, Kulkarni SB (2023) Classification of brain MR images based on bleed and calcification using ROI cropped U-net segmentation and ensemble RNN classifier. Int J Inform Technol https://link.springer.com/article/https://doi.org/10.1007/s41870-023-01389-2

  22. Dixit A, Thakur MK (2023) RVM-MR image brain tumour classification using novel statistical feature extraction. Int J Inform Technol 15:2395–2407. https://link.springer.com/article/https://doi.org/10.1007/s41870-023-01277-9

  23. Kulshreshtha A, Nagpal A (2023) Brain image segmentation using variation in structural elemets of morphological operators. Int J Inform Technolo. 15:2283–2291. https://link.springer.com/article/https://doi.org/10.1007/s41870-023-01252-4

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Authors and Affiliations

  1. Faculty of Science and Information Technology, Department of Computer Science, Jadara University, Irbid, Jordan

    Arwa Zabian

  2. Department of Computer Sciences Riyadh, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Ahmed Zohair Ibrahim

Authors
  1. Arwa Zabian
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  2. Ahmed Zohair Ibrahim
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Correspondence to Arwa Zabian.

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Zabian, A., Ibrahim, A.Z. Karnauph classifier for predicting breast cancer based on morphological features. Int. j. inf. tecnol. 16, 353–359 (2024). https://doi.org/10.1007/s41870-023-01607-x

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