BREAST CANCER CLASSIFICATION USING MACHINE LEARNING ALGORITHMS
DOI:
https://doi.org/10.61841/c9nx4y44Keywords:
support vector machine, Naïve Bayes, Decision tree, andom ForestAbstract
Cancer is the second reason behind death on the planet. Approximately eight million patients died because of cancer in 2019. The carcinoma is the leading reason for death amongst women. Several styles of studies are carried out on early detection of carcinoma to start remedy and growth the chance of survival. Most of the research concentrated on mammogram snapshots, MRI, and biopsy. However, mammograms, MRI, and biopsy photos have a risk of false detection that could endanger the patient's health. It is critical to hunt out alternative mechanisms that can be less complicated to implement and work with different information sets, which can be less expensive and safer, which may produce a greater dependable prediction. Classification, predictions are a form of the powerful processing strategies which are used to categorize and are expecting the records within the datasets, especially in a medical field, in which these strategies are widely utilized in prognosis and analysis to make decisions. The target of this paper is to match and perceive a correct model to predict the prevalence of carcinoma that supported various patient's medical records. The processing techniques make use of the gadget getting to know algorithms like a help vector device, naïve Bayes classifier, decision tree, Random Forest. It is anticipated that in actual application, physicians and patients can revel in the feature popularity outcome to prevent carcinoma, using these machines getting to know algorithms.
Downloads
References
1. R.Preetha, S. Vinila Jinny-A Research on Breast Cancer Prediction Using Data Mining Techniques.
2. Ch. Shravya, K. Pravalika, Shaik Subhani-Prediction of Breast Cancer Using Supervised Machine Learning Techniques.
3. Yi-Sheng Sun, Zhao hao, Han-Ping-Zhu, "Risk factors and Preventions of Breast Cancer" International Journal of Biological Sciences.
4. Mandeep Rana, Pooja Chandorkar, Alishiba Dsouza, "Breast cancer diagnosis and recurrence prediction using machine learning techniques", International Journal of Research in Engineering and Technology Volume 04, Issue 04, April 2015.
5. VikasChaurasia, BB Tiwari and Saurabh Pal – "Prediction of benign and malignant breast cancer using data mining techniques", Journal of Algorithms and Computational Technology.
6. Haifeng Wang and Sang Won Yoon – Breast Cancer Prediction Using Data Mining Method, IEEE Conference paper.
7. D.Dubey, S.Kharya, S.Soni and –"Predictive Machine Learning techniques for Breast Cancer Detection", International Journal of Computer Science and Information Technologies, Vol.4(6),2013.
8. Nidhi Mishra, NareshKhuriwal.- "Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm", 2018 IEEMA Engineer Infinite Conference (eTechNxT),2018.
9. Chao-Ying, Joanne, PengKukLida Lee, Gary M. Ingersoll –"An Introduction to Logistic Regression Analysis and Reporting ", September/October 2002 [Vol. 96(No.1)] Logistic Regression for Machine Learning.
10.
11. Mohammad Bolandraftar and Sadegh Bafandeh Imandoust - “Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background”- International Journal of Engineering Research and Applications Vol. 3, Issue 5, Sep-Oct 2013
12. EbrahimEdrissEbrahim Ali1, Wu Zhi Feng2- "Breast Cancer Classification using Support Vector Machine and Neural Network"– InternationalJournalofScienceandResearch(IJSR) Volume 5 Issue 3, March 2016
13. Padmaja P and B. Lakshmi Ramani. “Adaptive Fuzzy System with Robust GSCA-based Fuzzy Rule Extraction for Data Classification,” JARDCS, Vol. 10, 01, 2018.
14. Tumuluru, P. and Ravi, B. “GOA-based DBN: Grasshopper Optimization Algorithm-based Deep Belief Neural Networks for Cancer Classification”. International Journal of Applied Engineering Research 12
(24) (2017).
15. Tumuluru, P. and Ravi, B. "Chronological Grasshopper Optimization Algorithm- based Gene Selection and Cancer Classification. Journal of Advanced Research in Dynamical & Control Systems, Vol. 10, No. 3, 2018.
16. Praveen Tumuluru, Bhramaramba R, "A Framework for Identifying of Gene to Gene Mutation causing Lung Cancer using SPI - Network", International Journal of Computer Applications, vol. 152, no. 10, Oct 2016.
17. Praveen T, et al. "Credentials of Lung-Cancer Associated Genes Using Protein-Protein Interaction Network", International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 6, No. 3, March 2016.
18. Praveen Tumuluru, Bhramaramba Ravi "Dijkstra’s based Identification of Lung Cancer Related Genes using PPI Networks", IJCA, Vol. 163, No. 10, 04-2017.
19. Praveen T, Bhramaramba Ravi "A Survey on Gene Expression Classification Systems", International Journal of Scientific Research and Review ISSN NO: 2279-543X, Volume 6, Issue 12, 2017.
20. Praveen Tumuluru, Burra Lakshmi Ramani et al. "OpenCV Algorithms for facial recognition", International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-8 June, 2019.
21. B. Lakshmi Ramani, Praveen T et al. “Deep Learning and Fuzzy Rule-Based Hybrid Fusion Model for Data Classification” IJRTE, ISSN: 2277-3878, Volume-8 Issue-2, July 2019.
22. Praveen Tumuluru, Radha Manohar Jonnalagadda et al. “Extreme Learning Model Based Phishing Classifier” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019.
23. B. Lakshmi Ramani, Dr. Padmaja Poosapati “Adaptive Lion Fuzzy System to Generate the Classification Rules using Membership Functions based on Uniform Distribution” International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 24 (2017) pp. 14421-14433.
24. Tumuluru, P., Lakshmi, C.P., Sahaja, T., Prazna, R. “A Review of Machine Learning Techniques for Breast Cancer Diagnosis in Medical Applications “Proceedings of the 3rd International Conference on
I-SMAC IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2019.
25. Nalajala, S., Akhil, K., Sai, V., Shekhar, D.C., Tumuluru, P. “Light Weight Secure Data Sharing Scheme for Mobile Cloud Computing” Proceedings of the 3rd International Conference on I-SMAC IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2019.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
