Developing an Intelligent Tool for Breast Cancer Prognosis Using Artificial Neural Network
,
Abstract
Today, there is ample scientific evidence that Breast Cancer (BC) is a global health challenge given its prevalence and invasive nature. Therefore, early detection of BC can help minimize the devastating effects of the disease. This study aimed to design a Clinical Decision Support System (CDSS) based on the best Artificial Neural Network (ANN) configuration to identify patients quickly. Using a single-center registry, we retrospectively reviewed the records of 3380 suspected BC cases. The independence test of Chi-Square at P<0.01 was utilized to select the most important criteria. Then the different ANN configuration was implemented in the Matlab R2013 environment and compared using some evaluation criteria. Finally, the best ANN configuration was obtained. After implementing feature selection, 20 variables were determined as the most relevant factors. The experimental results indicate that the best performance was obtained by the 20-25-1 configuration with PPV=90.9%, NPV=99.7%, Sensitivity=98.9%, Specificity=97.9%, Accuracy=98.1%, and AUC=0.958. The proposed software can identify cases of BC from healthy individuals with optimal diagnostic accuracy. Additionally, it might be integrated as a practical and helpful tool in natural clinical settings for easy and effective disease screening.
1. Babiera GV. Metastatic breast cancer. A paradigm shift toward a more aggressive approach. Cancer Journal. 2009;15(1):78.
2. Dawngliani M, Chandrasekaran N, Lalmuanawma S, Thangkhanhau H, editors. Prediction of Breast Cancer Recurrence Using Ensemble Machine Learning Classifiers. International Conference on Security with Intelligent Computing and Big-data Services; 2019: Springer.
3. Cardoso F, Kyriakides S, Ohno S, Penault-Llorca F, Poortmans P, Rubio IT, et al. Erratum: ERRATUM: Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up (Ann Oncol (2019) (128) DOI: 10.1093/annonc/mdz173). Annals of Oncology. 2019;30(10):1674.
4. Mehdy MM, Ng PY, Shair EF, Saleh NIM, Gomes C. Artificial neural networks in image processing for early detection of breast cancer. Computational and Mathematical Methods in Medicine. 2017;2017.
5. Vijayasarveswari V, Jusoh M, Sabapathy T, Raof RAA, Khatun S, Iszaidy I, editors. Reliable Early Breast Cancer Detection using Artificial Neural Network for Small Data Set. Journal of Physics: Conference Series; 2021.
6. Dunderdale J, Kulkarni S. Assessment of Practice Patterns Following Publication of the SSO-ASTRO Consensus Guideline on Margins for Breast-Conserving Therapy in Stage i and II Invasive Breast Cancer DeSnyder SM, Hunt KK, Smith BD, et al (The Univ of Texas MD Anderson Cancer Ctr, Houston; Et al) Ann Surg Oncol 22:3250-3256, 2015. Breast Diseases. 2016;27(2):149-50.
7. Whitman GJ. Consequences of axillary ultrasound in patients with T2 or greater invasive breast cancers: Lee MC, Eatrides J, Chau A, et al (Moffitt Cancer Ctr, Tampa, FL; Univ of South Florida College of Medicine, Tampa) Ann Surg Oncol 18:72-77, 2011. Breast Diseases. 2011;22(4):373-4.
8. Salod Z, Singh Y. Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol. Journal of Public Health Research. 2019;8(3).
9. Beachler DC, de Luise C, Yin R, Gangemi K, Cochetti PT, Lanes S. Predictive model algorithms identifying early and advanced stage ER+/HER2− breast cancer in claims data. Pharmacoepidemiology and Drug Safety. 2019;28(2):171-8.
10. Franzoi MA, Rosa DD, Zaffaroni F, Werutsky G, Simon S, Bines J, et al. Advanced stage at diagnosis and worse clinicopathologic features in young women with breast cancer in Brazil: A subanalysis of the amazona III study (GBECAM 0115). Journal of Global Oncology. 2019;2019(5).
11. Allaire BT, Ekweme D, Hoerger TJ, DeGroff A, Rim SH, Subramanian S, et al. Cost-effectiveness of patient navigation for breast cancer screening in the National Breast and Cervical Cancer Early Detection Program. Cancer Causes and Control. 2019;30(9):923-9.
12. Benson J. Comparison of the indocyanine green fluorescence and blue dye methods in detection of sentinel lymph nodes in early-stage breast cancer: Sugie T, Sawada T, Tagaya N, et al (Kyoto Univ, Japan; Showa Univ, Tokyo, Japan; Dokkyo Med Univ, Koshigaya, Japan; Et al) Ann Surg Oncol 20:2213-2218, 2013. Breast Diseases. 2013;24(4):374-6.
13. Corrigendum: Prevention and screening in BRCA mutation carriers and other breast/ovarian hereditary cancer syndromes: ESMO Clinical Practice Guidelines for cancer prevention and screening [Ann Oncol, 27 (Suppl 5), (2016) (v103-v110)]doi 10.1093/annonc/mdw327. Annals of Oncology. 2017;28:iv168.
14. Domeyer PRJ, Sergentanis TN. New Insights into the Screening, Prompt Diagnosis, Management, and Prognosis of Breast Cancer. Journal of Oncology. 2020;2020.
15. Mohamed NC, Moey S-F, Lim B-C. Validity and reliability of health belief model questionnaire for promoting breast self-examination and screening mammogram for early cancer detection. Asian Pacific journal of cancer prevention: APJCP. 2019;20(9):2865.
16. Anderson BO, Bevers TB, Carlson RW. Clinical breast examination and breast cancer screening guideline. Jama. 2016;315(13):1403-4.
17. Alka K, Gupta RK. Breast Cancer Prediction Through Multilayer Artificial Neural Network. Lecture Notes in Networks and Systems2021. p. 203-14.
18. Al-Salihy NK, Ibrikci T, editors. Classifying breast cancer by using decision tree algorithms. Proceedings of the 6th International Conference on Software and Computer Applications; 2017.
19. Halim E, Halim PP, Hebrard M, editors. Artificial intelligent models for breast cancer early detection. 2018 International Conference on Information Management and Technology (ICIMTech); 2018: IEEE.
20. Chaurasia V, Pal S. Applications of machine learning techniques to predict diagnostic breast cancer. SN Computer Science. 2020;1(5):1-11.
21. Sathya D, Sudha V, Jagadeesan D. Application of Machine Learning Techniques in Healthcare. Handbook of Research on Applications and Implementations of Machine Learning Techniques: IGI Global; 2020. p. 289-304.
22. Heidari M, Khuzani AZ, Hollingsworth AB, Danala G, Mirniaharikandehei S, Qiu Y, et al. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm. Physics in Medicine & Biology. 2018;63(3):035020.
23. Yue W, Wang Z, Chen H, Payne A, Liu X. Machine learning with applications in breast cancer diagnosis and prognosis. Designs. 2018;2(2):1-17.
24. Mohammed SA, Darrab S, Noaman SA, Saake G, editors. Analysis of breast cancer detection using different machine learning techniques. International Conference on Data Mining and Big Data; 2020: Springer.
25. Peng J, Zeng X, Townsend J, Liu G, Huang Y, Lin S. A Machine Learning Approach to Uncovering Hidden Utilization Patterns of Early Childhood Dental Care Among Medicaid-Insured Children. Frontiers in Public Health. 2021;8.
26. Mohammed A, Arunachalam N, editors. Imbalanced Machine Learning Based Techniques for Breast Cancer Detection. 2021 International Conference on System, Computation, Automation and Networking (ICSCAN); 2021: IEEE.
27. Cai J, Luo J, Wang S, Yang S. Feature selection in machine learning: A new perspective. Neurocomputing. 2018;300:70-9.
28. Mwadulo MW. A Review on Feature Selection Methods For Classification Tasks. International Journal of Computer Applications Technology and Research. 2016;5(6):395-402.
29. Delzell DA, Magnuson S, Peter T, Smith M, Smith B. Machine learning and feature selection methods for disease classification with application to lung cancer screening image data. Frontiers in oncology. 2019;9:1393.
30. El-Hasnony IM, Barakat SI, Elhoseny M, Mostafa RR. Improved feature selection model for big data analytics. IEEE Access. 2020;8:66989-7004.
31. Maind SB, Wankar P. Research paper on basic of artificial neural network. International Journal on Recent Innovation Trends in Computing Communication. 2014;2(1):96-100.
32. Mishra M, Srivastava M, editors. A view of artificial neural network. 2014 International Conference on Advances in Engineering & Technology Research (ICAETR-2014); 2014: IEEE.
33. Da Silva IN, Spatti DH, Flauzino RA, Liboni LHB, dos Reis Alves SF. Artificial neural network architectures and training processes. Artificial neural networks: Springer; 2017. p. 21-8.
34. Zakaria M, Al-Shebany M, Sarhan S. Artificial neural network: a brief overview. International Journal of Engineering Research Applications. 2014;4(2):7-12.
35. Walczak S. Artificial neural networks. Encyclopedia of Information Science and Technology, Fourth Edition: IGI Global; 2018. p. 120-31.
36. Santini D, Taffurelli M, Gelli MC, Grassigli A, Giosa F, Marrano D, et al. Neoplastic involvement of nipple-areolar complex in invasive breast cancer. The American Journal of Surgery. 1989;158(5):399-403.
37. Massafra R, Latorre A, Fanizzi A, Bellotti R, Didonna V, Giotta F, et al. A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results. Frontiers in Oncology. 2021;11.
38. Papandreou P, Gioxari A, Nimee F, Skouroliakou M. Application of clinical decision support system to assist breast cancer patients with lifestyle modifications during the covid-19 pandemic: A randomised controlled trial. Nutrients. 2021;13(6).
39. Leszczyński Z, Jasiński T. Artificial Neural Networks in Forecasting Cancer Therapy Methods and Costs of Cancer Patient Treatment. Case Study for Breast Cancer. Advances in Intelligent Systems and Computing2020. p. 111-20.
40. Rawal G, Rawal R, Shah H, Patel K. A Comparative Study Between Artificial Neural Networks and Conventional Classifiers for Predicting Diagnosis of Breast Cancer. Lecture Notes in Electrical Engineering2020. p. 261-71.
41. Irmak MC, Tas MBH, Turan S, Hasiloglu A, editors. Comparative breast cancer detection with artificial neural networks and machine learning methods. SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings; 2021.
42. Naveed N, Madhloom HT, Husain MS. Breast cancer diagnosis using wrapper-based feature selection and artificial neural network. Applied Computer Science. 2021;17(3):19-30.
43. S P, Al-Turjman F, Stephan T. An automated breast cancer diagnosis using feature selection and parameter optimization in ANN. Computers and Electrical Engineering. 2021;90.
44. Hazra R, Banerjee M, Badia L, editors. Machine Learning for Breast Cancer Classification with ANN and Decision Tree. 11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020; 2020.
45. Singhal P, Pareek S, editors. Artificial neural network for Prediction of breast cancer. Proceedings of the International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2018; 2019.
46. Sepandi M, Taghdir M, Rezaianzadeh A, Rahimikazerooni S. Assessing breast cancer risk with an artificial neural network. Asian Pacific Journal of Cancer Prevention. 2018;19(4):1017-9.
2. Dawngliani M, Chandrasekaran N, Lalmuanawma S, Thangkhanhau H, editors. Prediction of Breast Cancer Recurrence Using Ensemble Machine Learning Classifiers. International Conference on Security with Intelligent Computing and Big-data Services; 2019: Springer.
3. Cardoso F, Kyriakides S, Ohno S, Penault-Llorca F, Poortmans P, Rubio IT, et al. Erratum: ERRATUM: Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up (Ann Oncol (2019) (128) DOI: 10.1093/annonc/mdz173). Annals of Oncology. 2019;30(10):1674.
4. Mehdy MM, Ng PY, Shair EF, Saleh NIM, Gomes C. Artificial neural networks in image processing for early detection of breast cancer. Computational and Mathematical Methods in Medicine. 2017;2017.
5. Vijayasarveswari V, Jusoh M, Sabapathy T, Raof RAA, Khatun S, Iszaidy I, editors. Reliable Early Breast Cancer Detection using Artificial Neural Network for Small Data Set. Journal of Physics: Conference Series; 2021.
6. Dunderdale J, Kulkarni S. Assessment of Practice Patterns Following Publication of the SSO-ASTRO Consensus Guideline on Margins for Breast-Conserving Therapy in Stage i and II Invasive Breast Cancer DeSnyder SM, Hunt KK, Smith BD, et al (The Univ of Texas MD Anderson Cancer Ctr, Houston; Et al) Ann Surg Oncol 22:3250-3256, 2015. Breast Diseases. 2016;27(2):149-50.
7. Whitman GJ. Consequences of axillary ultrasound in patients with T2 or greater invasive breast cancers: Lee MC, Eatrides J, Chau A, et al (Moffitt Cancer Ctr, Tampa, FL; Univ of South Florida College of Medicine, Tampa) Ann Surg Oncol 18:72-77, 2011. Breast Diseases. 2011;22(4):373-4.
8. Salod Z, Singh Y. Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol. Journal of Public Health Research. 2019;8(3).
9. Beachler DC, de Luise C, Yin R, Gangemi K, Cochetti PT, Lanes S. Predictive model algorithms identifying early and advanced stage ER+/HER2− breast cancer in claims data. Pharmacoepidemiology and Drug Safety. 2019;28(2):171-8.
10. Franzoi MA, Rosa DD, Zaffaroni F, Werutsky G, Simon S, Bines J, et al. Advanced stage at diagnosis and worse clinicopathologic features in young women with breast cancer in Brazil: A subanalysis of the amazona III study (GBECAM 0115). Journal of Global Oncology. 2019;2019(5).
11. Allaire BT, Ekweme D, Hoerger TJ, DeGroff A, Rim SH, Subramanian S, et al. Cost-effectiveness of patient navigation for breast cancer screening in the National Breast and Cervical Cancer Early Detection Program. Cancer Causes and Control. 2019;30(9):923-9.
12. Benson J. Comparison of the indocyanine green fluorescence and blue dye methods in detection of sentinel lymph nodes in early-stage breast cancer: Sugie T, Sawada T, Tagaya N, et al (Kyoto Univ, Japan; Showa Univ, Tokyo, Japan; Dokkyo Med Univ, Koshigaya, Japan; Et al) Ann Surg Oncol 20:2213-2218, 2013. Breast Diseases. 2013;24(4):374-6.
13. Corrigendum: Prevention and screening in BRCA mutation carriers and other breast/ovarian hereditary cancer syndromes: ESMO Clinical Practice Guidelines for cancer prevention and screening [Ann Oncol, 27 (Suppl 5), (2016) (v103-v110)]doi 10.1093/annonc/mdw327. Annals of Oncology. 2017;28:iv168.
14. Domeyer PRJ, Sergentanis TN. New Insights into the Screening, Prompt Diagnosis, Management, and Prognosis of Breast Cancer. Journal of Oncology. 2020;2020.
15. Mohamed NC, Moey S-F, Lim B-C. Validity and reliability of health belief model questionnaire for promoting breast self-examination and screening mammogram for early cancer detection. Asian Pacific journal of cancer prevention: APJCP. 2019;20(9):2865.
16. Anderson BO, Bevers TB, Carlson RW. Clinical breast examination and breast cancer screening guideline. Jama. 2016;315(13):1403-4.
17. Alka K, Gupta RK. Breast Cancer Prediction Through Multilayer Artificial Neural Network. Lecture Notes in Networks and Systems2021. p. 203-14.
18. Al-Salihy NK, Ibrikci T, editors. Classifying breast cancer by using decision tree algorithms. Proceedings of the 6th International Conference on Software and Computer Applications; 2017.
19. Halim E, Halim PP, Hebrard M, editors. Artificial intelligent models for breast cancer early detection. 2018 International Conference on Information Management and Technology (ICIMTech); 2018: IEEE.
20. Chaurasia V, Pal S. Applications of machine learning techniques to predict diagnostic breast cancer. SN Computer Science. 2020;1(5):1-11.
21. Sathya D, Sudha V, Jagadeesan D. Application of Machine Learning Techniques in Healthcare. Handbook of Research on Applications and Implementations of Machine Learning Techniques: IGI Global; 2020. p. 289-304.
22. Heidari M, Khuzani AZ, Hollingsworth AB, Danala G, Mirniaharikandehei S, Qiu Y, et al. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm. Physics in Medicine & Biology. 2018;63(3):035020.
23. Yue W, Wang Z, Chen H, Payne A, Liu X. Machine learning with applications in breast cancer diagnosis and prognosis. Designs. 2018;2(2):1-17.
24. Mohammed SA, Darrab S, Noaman SA, Saake G, editors. Analysis of breast cancer detection using different machine learning techniques. International Conference on Data Mining and Big Data; 2020: Springer.
25. Peng J, Zeng X, Townsend J, Liu G, Huang Y, Lin S. A Machine Learning Approach to Uncovering Hidden Utilization Patterns of Early Childhood Dental Care Among Medicaid-Insured Children. Frontiers in Public Health. 2021;8.
26. Mohammed A, Arunachalam N, editors. Imbalanced Machine Learning Based Techniques for Breast Cancer Detection. 2021 International Conference on System, Computation, Automation and Networking (ICSCAN); 2021: IEEE.
27. Cai J, Luo J, Wang S, Yang S. Feature selection in machine learning: A new perspective. Neurocomputing. 2018;300:70-9.
28. Mwadulo MW. A Review on Feature Selection Methods For Classification Tasks. International Journal of Computer Applications Technology and Research. 2016;5(6):395-402.
29. Delzell DA, Magnuson S, Peter T, Smith M, Smith B. Machine learning and feature selection methods for disease classification with application to lung cancer screening image data. Frontiers in oncology. 2019;9:1393.
30. El-Hasnony IM, Barakat SI, Elhoseny M, Mostafa RR. Improved feature selection model for big data analytics. IEEE Access. 2020;8:66989-7004.
31. Maind SB, Wankar P. Research paper on basic of artificial neural network. International Journal on Recent Innovation Trends in Computing Communication. 2014;2(1):96-100.
32. Mishra M, Srivastava M, editors. A view of artificial neural network. 2014 International Conference on Advances in Engineering & Technology Research (ICAETR-2014); 2014: IEEE.
33. Da Silva IN, Spatti DH, Flauzino RA, Liboni LHB, dos Reis Alves SF. Artificial neural network architectures and training processes. Artificial neural networks: Springer; 2017. p. 21-8.
34. Zakaria M, Al-Shebany M, Sarhan S. Artificial neural network: a brief overview. International Journal of Engineering Research Applications. 2014;4(2):7-12.
35. Walczak S. Artificial neural networks. Encyclopedia of Information Science and Technology, Fourth Edition: IGI Global; 2018. p. 120-31.
36. Santini D, Taffurelli M, Gelli MC, Grassigli A, Giosa F, Marrano D, et al. Neoplastic involvement of nipple-areolar complex in invasive breast cancer. The American Journal of Surgery. 1989;158(5):399-403.
37. Massafra R, Latorre A, Fanizzi A, Bellotti R, Didonna V, Giotta F, et al. A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results. Frontiers in Oncology. 2021;11.
38. Papandreou P, Gioxari A, Nimee F, Skouroliakou M. Application of clinical decision support system to assist breast cancer patients with lifestyle modifications during the covid-19 pandemic: A randomised controlled trial. Nutrients. 2021;13(6).
39. Leszczyński Z, Jasiński T. Artificial Neural Networks in Forecasting Cancer Therapy Methods and Costs of Cancer Patient Treatment. Case Study for Breast Cancer. Advances in Intelligent Systems and Computing2020. p. 111-20.
40. Rawal G, Rawal R, Shah H, Patel K. A Comparative Study Between Artificial Neural Networks and Conventional Classifiers for Predicting Diagnosis of Breast Cancer. Lecture Notes in Electrical Engineering2020. p. 261-71.
41. Irmak MC, Tas MBH, Turan S, Hasiloglu A, editors. Comparative breast cancer detection with artificial neural networks and machine learning methods. SIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedings; 2021.
42. Naveed N, Madhloom HT, Husain MS. Breast cancer diagnosis using wrapper-based feature selection and artificial neural network. Applied Computer Science. 2021;17(3):19-30.
43. S P, Al-Turjman F, Stephan T. An automated breast cancer diagnosis using feature selection and parameter optimization in ANN. Computers and Electrical Engineering. 2021;90.
44. Hazra R, Banerjee M, Badia L, editors. Machine Learning for Breast Cancer Classification with ANN and Decision Tree. 11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020; 2020.
45. Singhal P, Pareek S, editors. Artificial neural network for Prediction of breast cancer. Proceedings of the International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2018; 2019.
46. Sepandi M, Taghdir M, Rezaianzadeh A, Rahimikazerooni S. Assessing breast cancer risk with an artificial neural network. Asian Pacific Journal of Cancer Prevention. 2018;19(4):1017-9.
| Files | ||
| Issue | Vol 60 No 9 (2022) | |
| Section | Articles | |
| DOI | https://doi.org/10.18502/acta.v60i9.11097 | |
| Keywords | ||
| Artificial intelligence Machine learning Data mining Breast cancer Artificial neural network | ||
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How to Cite
1.
Nopour R, Shanbehzadeh M, Mehrabi N. Developing an Intelligent Tool for Breast Cancer Prognosis Using Artificial Neural Network. Acta Med Iran. 2022;60(9):562-570.
