Breast Cancer Screening and Diagnosis using AI : Analyzing the Role of Feature Engineering

Authors

  • Mr. M. Sriivasulu  M.Tech Student, Department of ECE, AITS, Tirupati, Andhra Pradesh, India
  • Dr. I. Suneetha  Professor, M.Tech, Ph.D., Department of ECE, AITS, Tirupati, Andhra Pradesh, India
  • Dr. N. Pushpalatha  Professor and HOD, M.Tech, Ph.D., Department of ECE, AITS, Tirupati, Andhra Pradesh, India

Keywords:

Logistic Regression, SVC, Random Forest, Decision Tree, Cat Boost, K Neighbours, MLP Classifier and GaussianNB.

Abstract

Breast cancer, according to the Breast Cancer Institute (BCI), is a highly dangerous disease that affects women worldwide. Medical professionals stress the crucial role of early detection in saving lives. The website cancer.net provides personalized guidance for over 120 cancer types and related hereditary conditions. In the field of breast cancer detection, machine learning techniques are frequently used. In our study, we have introduced an adaptive ensemble voting approach for diagnosing breast cancer using the Wisconsin Breast Cancer database. Our primary aim is to compare and clarify how the logistic algorithm can achieve superior results when combined with ensemble machine learning methods for breast cancer diagnosis, even with a reduced number of variables. Breast tumors fall into two categories: benign tumors, which are non-cancerous, and malignant tumors, which are cancerous.

References

  1. M. R. Al-Hadidi, A. Alarabeyyat and M. Alhanahnah, "Breast Cancer Detection Using K-Nearest Neighbor Machine Learning Algorithm," 2016 9th International Conference on Developments in eSystems Engineering (DeSE), Liverpool, 2016, pp. 35-39.
  2. C. Deng and M. Perkowski, "A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection," 2015 IEEE International Symposium on Multiple-Valued Logic, Waterloo, ON, 2015, pp. 115-120.
  3. A. Qasem et al., "Breast cancer mass localization based on machine learning," 2014 IEEE 10th International Colloquium on Signal Processing and its Applications, Kuala Lumpur, 2014, pp. 31-36.
  4. A. Osareh and B. Shadgar, "Machine learning techniques to diagnose breast cancer," 2010 5th International Symposium on Health Informatics and Bioinformatics, Antalya, 2010, pp. 114-120.
  5. J. A. Bhat, V. George and B. Malik, "Cloud Computing with Machine Learning Could Help Us in the Early Diagnosis of Breast Cancer," 2015 Second International Conference on Advances in Computing and Communication Engineering, Dehradun, 2015, pp. 644-648.
  6. B. M. Gayathri and C. P. Sumathi, "Comparative study of relevance vector machine with various machine learning techniques used for detecting breast cancer," 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, 2016, pp. 1-5.
  7. H. R. Mhaske and D. A. Phalke, "Melanoma skin cancer detection and classification based on supervised and unsupervised learning," 2013 International conference on Circuits, Controls and Communications (CCUBE), Bengaluru, 2013, pp. 1-5.
  8. S. Aruna, S. P. Rajagopalan and L. V. Nandakishore, "An algorithm proposed for Semi-Supervised learning in cancer detection," International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011), Chennai, 2011, pp. 860-864.
  9. Y. Tsehay et al., "Biopsy-guided learning with deep convolutional neural networks for Prostate Cancer detection on multiparametric MRI," 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, 2017, pp. 642-645.
  10. S. Nayak and D. Gope, "Comparison of supervised learning algorithms for RF-based breast cancer detection," 2017 Computing and Electromagnetics International Workshop (CEM), Barcelona, 2017, pp. 13-14. doi: 10.1109/CEM.2017.7991863
  11. P. Harish, S. Baskar, “ A survey on detection, recognition, segmentation and classification of brain tumor,” Int. J. Eng. Adv. Technol. 8 (2) (2019) 249–254.
  12. P. Harish, S. Baskar, “ Safe engineering applications for diagnosis of brain abnormalities using adaptive local binary pattern based texture analysis,” J.Green Eng. 10 (9) (2020) 4727–4743
  13. Harish Pasupulati and Baskar S (2020) “Safe engineering methods for detecting brain tumor using heat distribution merged saliency detection,” Journal of Green Engineering, 10(10), pp. 7946–7961. http://www.jgenng.com/volume10-issue 10.php
  14. Chandrakala Kuruba, N. Pushpalatha, Gandikota Ramu, I. Suneetha, M. Rudra Kumar, P. Harish “Data mining and deep learning-based hybrid health care application,” Applied Nanoscience (Switzerland), 2023, 13(3), pp. 2431-2437. 
  15. Ramani K, Suneetha I, Pushpalatha N, Harish P, Yugundhar P, “Chronic Disease Prediction through Supervised Learning Techniques,” Journal of Pharmaceutical Negative Results, 2022, 13(4), pp. 990-998.
  16. Ramani K, Suneetha I, Pushpalatha N, Harish P, Yugundhar P, “Efficient Multi-Object Identification and Guiding System for Visually Impaired,” Journal of Pharmaceutical Negative Results, 2022, 13(4), pp. 1019-1023.
  17. Silpa C, Suneetha I, Reddy Hemantha G, Arava R.P.R, Bhumika Y, “Medication Alarm: A Proficient IoT -Enabled Medication Alarm for Age Old People to the Betterment of their Medication Practice,” Journal of Pharmaceutical Negative Results, 2022, 13(4), pp. 1041-1046.
  18. Suneetha I, Venkateswarlu T, “Noise suppression using non-linear PAR model for binary images,” 2013 15th International Conference on Advanced Computing Technologies (ICACT), Rajampet, India 2013 https://ieeexplore.ieee.org/document/6710525.
  19. Suneetha I, Venkateswarlu T, “Image enhancement through contrast improvement using linear parameterized gradient intercept model,” ARPN Journal of Engineering and Applied Sciences, 2012, 7(8), pp. 945-949.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2023-12-30

Issue

Volume 10, Issue 6, November-December-2023

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Mr. M. Sriivasulu, Dr. I. Suneetha, Dr. N. Pushpalatha, " Breast Cancer Screening and Diagnosis using AI : Analyzing the Role of Feature Engineering, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 10, Issue 6, pp.29-38, November-December-2023.