Breast Cancer Prediction Web Model

Ashish Chauhan; Kajal Kori; Priti Pal; Khushi Sani

doi:10.32628/IJSRST24113102

Authors

Ashish Chauhan Hod, Department of CSE, Shri Ram Group of Colleges, Muzaffarnagar, Uttar Pradesh, India Author
Kajal Kori B. Tech Scholars, Department of CSE, Shri Ram Group of Colleges, Muzaffarnagar, Uttar Pradesh, India Author
Priti Pal B. Tech Scholars, Department of CSE, Shri Ram Group of Colleges, Muzaffarnagar, Uttar Pradesh, India Author
Khushi Sani B. Tech Scholars, Department of CSE, Shri Ram Group of Colleges, Muzaffarnagar, Uttar Pradesh, India Author

DOI:

https://doi.org/10.32628/IJSRST24113102

Keywords:

Machine Learning Algorithms, ML Classification Techniques, DT, KNN, SVM, RF, NB, LR, WDBC Data Set.

Abstract

In this era cancer is biggest problem for human resource and there is multiple type of cancer like blood cancer, breast cancer etc. but here we are talk about only breast cancer. Breast cancer is generally faced by women. Every year the cases of breast cancer increase rapidly and as well as death rate also. The breast cancer is the cause of death in women worldwide. To make breast cancer prediction model is a very challenging task because high accuracy in prediction is very important to know the actual condition or results in the form of chances of survival. Through Machine Learning the prediction and early diagnosis can be easy by using different techniques and algorithms of Machine Learning. But here we use classification method to predict the breast cancer.

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References

S Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 71(3), 209-249. DOI: https://doi.org/10.3322/caac.21660

Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D. M., Piñeros, M., Znaor, A., & Bray, F. (2021). Cancer statistics for the year 2020: An overview. International Journal of Cancer, 149(4), 778-789. DOI: https://doi.org/10.1002/ijc.33588

Patil S, Kirange D, Nemade V. Predictive modelling of brain tumor detection using deep learning. Journal of Critical Reviews. 2020;7.

Nemade, V., Pathak, S., Dubey, A. K., & Barhate, D. (2022).A Review and Computational Analysis of Breast Cancer Using Different Machine Learning Techniques,12(3)111-118. DOI: https://doi.org/10.46338/ijetae0322_13

Chaturvedi P, Jhamb A, Vanani M, Nemade V. Prediction and classification of lung cancer using machine learning techniques. InIOP Conference Series: Materials Science and Engineering 2021 Mar 1 (Vol. 1099, No. 1, p. 012059). IOP Publishing. DOI: https://doi.org/10.1088/1757-899X/1099/1/012059

Abdelhafiz, D., Yang, C., Ammar, R., & Nabavi, S. (2019). Deep convolutional neural networks for mammography: advances, challenges and applications. BMC bioinformatics, 20(11), 1-20. DOI: https://doi.org/10.1186/s12859-019-2823-4

Nemade, V., Pathak, S., & Dubey, A. K. (2022). A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques. Archives of Computational Methods in Engineering, 1-30. DOI: https://doi.org/10.1007/s11831-022-09738-3

Dora L, Agrawal S, Panda R, Abraham A. (2017) Optimal breast cancer classification using Gauss–Newton representation-based algorithm. Expert Systems with Applications,85:134-45. DOI: https://doi.org/10.1016/j.eswa.2017.05.035

Obaid OI, Mohammed MA, Ghani MK, Mostafa A, Taha F. (2018) Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer. International Journal of Engineering & Technology,7(4.36):160-6.

Yellamma, P., Chowdary, C. S., Karunakar, G., Rao, B. S., & Ganesan, V. (2020). Breast Cancer Diagnosis Using MLP Back Propagation. International Journal, 8(9). DOI: https://doi.org/10.30534/ijeter/2020/102892020

Naji, M. A., El Filali, S., Aarika, K., Benlahmar, E. H., Abdelouhahid, R. A., & Debauche, O. (2021). Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis. Procedia Computer Science, 191, 487-492. DOI: https://doi.org/10.1016/j.procs.2021.07.062

Kabiraj, S., Raihan, M., Alvi, N., Afrin, M., Akter, L., Sohagi, S. A., & Podder, E. (2020, July). Breast cancer risk prediction using XGBoost and random forest algorithm. In 2020 11th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1-4). IEEE. DOI: https://doi.org/10.1109/ICCCNT49239.2020.9225451

Liu, P., Fu, B., Yang, S. X., Deng, L., Zhong, X., & Zheng, H. (2020). Optimizing survival analysis of XGBoost for ties to predict disease progression of breast cancer. IEEE Transactions on Biomedical Engineering, 68(1), 148-160. DOI: https://doi.org/10.1109/TBME.2020.2993278

Nanglia, S., Ahmad, M., Khan, F. A., & Jhanjhi, N. Z. (2022). An enhanced Predictive heterogeneous ensemble model for breast cancer prediction. Biomedical Signal Processing and Control, 72, 103279. DOI: https://doi.org/10.1016/j.bspc.2021.103279

Islam, M., Haque, M., Iqbal, H., Hasan, M., Hasan, M., & Kabir, M. N. (2020). Breast cancer prediction: a comparative study using machine learning techniques. SN Computer Science, 1(5), 1-14. DOI: https://doi.org/10.1007/s42979-020-00305-w

Amrane, M., Oukid, S., Gagaoua, I., & Ensari, T. (2018, April). Breast cancer classification using machine learning. In 2018 electric electronics, computer science, biomedical engineerings' meeting (EBBT) (pp. 1-4). IEEE. DOI: https://doi.org/10.1109/EBBT.2018.8391453

Dhahri, H., Al Maghayreh, E., Mahmood, A., Elkilani, W., & Faisal Nagi, M. (2019). Automated breast cancer diagnosis based on machine learning algorithms. Journal of healthcare engineering. DOI: https://doi.org/10.1155/2019/4253641

Kharya S, Soni S. Weighted naive bayes classifier: a predictive model for breast cancer detection. International Journal of Computer Applications. 2016 Jan;133(9):32-7. DOI: https://doi.org/10.5120/ijca2016908023

Huang Q, Chen Y, Liu L, Tao D, Li X. On combining biclustering mining and AdaBoost for breast cancer classification. IEEE Transactions on Knowledge and Data Engineering. 2019 Jan 14;32(4):728-38. DOI: https://doi.org/10.1109/TKDE.2019.2891622

Ara S, Das A, Dey A. Malignant and benign breast cancer classification using machine learning algorithms. In2021 International Conference on Artificial Intelligence (ICAI) 2021 Apr 5 (pp. 97-101). IEEE. DOI: https://doi.org/10.1109/ICAI52203.2021.9445249

Jabbar MA. Breast cancer data classification using ensemble machine learning. Engineering and Applied Science Research. 2021 Jan 27;48(1):65-72.

Mahesh TR, Vinoth Kumar V, Vivek V, Karthick Raghunath KM, Sindhu Madhuri G. Early predictive model for breast cancer classification using blended ensemble learning. International Journal of System Assurance Engineering and Management. 2022 Jun 26:1-0. DOI: https://doi.org/10.1007/s13198-022-01696-0

https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)

Chaurasia V, Pal S, Tiwari BB. Prediction of benign and malignant breast cancer using data mining techniques.