LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,149
تعداد مقالات10,518
تعداد مشاهده مقاله45,415,478
تعداد دریافت فایل اصل مقاله11,291,251
Rabiei, Reza, Ayyoubzadeh, Seyed Mohammad, Sohrabei, Solmaz, Esmaeili, Marzieh, Atashi, Alireza. (1401). Prediction of Breast Cancer using Machine Learning Approaches. سامانه مدیریت نشریات علمی, 12(3), 297-308. doi: 10.31661/jbpe.v0i0.2109-1403
Reza Rabiei; Seyed Mohammad Ayyoubzadeh; Solmaz Sohrabei; Marzieh Esmaeili; Alireza Atashi. "Prediction of Breast Cancer using Machine Learning Approaches". سامانه مدیریت نشریات علمی, 12, 3, 1401, 297-308. doi: 10.31661/jbpe.v0i0.2109-1403
Rabiei, Reza, Ayyoubzadeh, Seyed Mohammad, Sohrabei, Solmaz, Esmaeili, Marzieh, Atashi, Alireza. (1401). 'Prediction of Breast Cancer using Machine Learning Approaches', سامانه مدیریت نشریات علمی, 12(3), pp. 297-308. doi: 10.31661/jbpe.v0i0.2109-1403
Rabiei, Reza, Ayyoubzadeh, Seyed Mohammad, Sohrabei, Solmaz, Esmaeili, Marzieh, Atashi, Alireza. Prediction of Breast Cancer using Machine Learning Approaches. سامانه مدیریت نشریات علمی, 1401; 12(3): 297-308. doi: 10.31661/jbpe.v0i0.2109-1403

Prediction of Breast Cancer using Machine Learning Approaches

Journal of Biomedical Physics and Engineering
مقاله 9، دوره 12، شماره 3، شهریور 2022، صفحه 297-308    اصل مقاله (910.24 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2109-1403
نویسندگان
Reza Rabiei1؛ Seyed Mohammad Ayyoubzadeh2؛ Solmaz Sohrabei* 3؛ Marzieh Esmaeili2؛ Alireza Atashi4
1PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2PhD, Department of Health Information Technology and Management, School of Allied Medical Sciences, Tehran University of Medical Science, Tehran, Iran
3MSc, Department Deputy of Development, Management and Resources, Office of Statistic and Information Technology Management, Zanjan University of Medical Sciences, Zanjan, Iran
4PhD, Department of E-Health, Virtual School, Tehran University of Medical Sciences, Medical Informatics Research Group, Clinical Research Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
چکیده
Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data.
Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data.
Material and Methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer.
Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network.
Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.
کلیدواژه‌ها
Artificial Intelligence؛ Breast Cancer؛ Computing Methodologies؛ Genetic Algorithm؛ Machine Learning
سایر فایل های مرتبط با مقاله
مراجع
  1. Zhang X, Shengli SU, Hongchao WA. Intelligent diagnosis model and method of palpation imaging breast cancer based on data mining. Big Data Research. 2019;5(1):2019005. doi: 10.11959/j.issn.2096-0271.2019005.
  2. Chen SI, Tseng HT, Hsieh CC. Evaluating the impact of soy compounds on breast cancer using the data mining approach. Food & function. 2020;11(5):4561-70. doi: 10.1039/C9FO00976K. PubMed PMID: 32400770.
  3. Aavula R, Bhramaramba R, Ramula US. A Comprehensive Study on Data Mining Techniques used in Bioinformatics for Breast Cancer Prognosis. Journal of Innovation in Computer Science and Engineering. 2019;9(1):34-9.
  4. Kaushik D, Kaur K. Application of Data Mining for high accuracy prediction of breast tissue biopsy results. 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC); Moscow, Russia: IEEE; 2016. p. 40-5. doi: 10.1109/DIPDMWC.2016.7529361.
  5. Mokhtar SA, Elsayad A. Predicting the severity of breast masses with data mining methods. ArXiv preprint arXiv:1305.7057. 2013. doi: 10.48550/ARxIV.1305.7057.
  6. Chaurasia V, Pal S, Tiwari BB. Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology. 2018;12(2):119-26. doi: 10.1177/1748301818756225.
  7. Fan J, Wu Y, Yuan M, Page D, Liu J, Ong IM, Peissig P, Burnside E. Structure-leveraged methods in breast cancer risk prediction. The Journal of Machine Learning Research. 2016;17(1):2956-70.
  8. Burnside ES, Liu J, Wu Y, Onitilo AA, McCarty CA, Page CD, et al. Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy. Acad Radiol. 2016;23(1):62-9. doi: 10.1016/j.acra.2015.09.007. PubMed PMID: 26514439. PubMed PMCID: PMC4684977.
  9. Stephens K. New Mammogram Measures of Breast Cancer Risk Could Revolutionize Screening. AXIS Imaging News. 2020.
  10. Feld SI, Fan J, Yuan M, Wu Y, Woo KM, Alexandridis R, Burnside ES. Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age. AMIA Jt Summits Transl Sci Proc. 2018;2017:81-90. PubMed PMID: 29888046. PubMed PMCID: PMC5961791.
  11. Guan Y, Nehl E, Pencea I, Condit CM, Escoffery C, Bellcross CA, McBride CM. Willingness to decrease mammogram frequency among women at low risk for hereditary breast cancer. Sci Rep. 2019;9(1):9599. doi: 10.1038/s41598-019-45967-6. PubMed PMID: 31270367. PubMed PMCID: PMC6610104.
  12. American Cancer Society. Cancer facts & figures 2018. Atlanta: American Cancer Society; 2018.
  13. Blandin Knight S, Crosbie PA, Balata H, Chudziak J, Hussell T, Dive C. Progress and prospects of early detection in lung cancer. Open Biol. 2017;7(9):170070. doi: 10.1098/rsob.170070. PubMed PMID: 28878044. PubMed PMCID: PMC5627048.
  14. Jothi N, Husain W. Data mining in healthcare-a review. Procedia Computer Science. 2015;72:306-13. doi: 10.1016/j.procs.2015.12.145.
  15. Maxwell K, Nathanson K. Common breast cancer risk variants in the post-COGS era: a comprehensive review. Breast Cancer Res. 2013;15(6):212. doi: 10.1186/bcr3591. PubMed PMID: 24359602. PubMed PMCID: PMC3978855.
  16. McCarthy AM, Keller B, Kontos D, Boghossian L, McGuire E, Bristol M, et al. The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms. Breast Cancer Res. 2015;17(1). doi: 10.1186/s13058-014-0509-4. PubMed PMID: 25567532. PubMed PMCID: PMC4311477.
  17. Bent CK, Bassett LW, D’Orsi CJ, Sayre JW. The positive predictive value of BI-RADS microcalcification descriptors and final assessment categories. AJR Am J Roentgenol. 2010;194(5):1378-83. doi: 10.2214/AJR.09.3423. PubMed PMID: 20410428.
  18. Burnside ES, Rubin DL, Fine JP, Shachter RD, Sisney GA, Leung WK. Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience. Radiology. 2006;240(3):666-73. doi: 10.1148/radiol.2403051096. PubMed PMID: 16926323.
  19. Ghani MU, Alam TM, Jaskani FH. Comparison of classification models for early prediction of breast cancer. 2019 International Conference on Innovative Computing (ICIC); Lahore, Pakistan: IEEE; 2019. p. 1-6. doi: 10.1109/ICIC48496.2019.8966691.
  20. Williams K, Idowu PA, Balogun JA, Oluwaranti AI. Breast cancer risk prediction using data mining classification techniques. Transactions on Networks and Communications. 2015;3(2):1-11. doi: 10.14738/tnc.32.662.
  21. Ferreira P, Fonseca NA, Dutra I, Woods R, Burnside E. Predicting malignancy from mammography findings and image-guided core biopsies. International Journal of Data Mining and Bioinformatics. 2015;11(3):257-76. doi: 10.1504/IJDMB.2015.067319. PubMed PMID: 26333262. PubMed PMCID: PMC4764253.
  22. Oyewola D, Hakimi D, Adeboye K, Shehu MD. Using five machine learning for breast cancer biopsy predictions based on mammographic diagnosis. International Journal of Engineering Technologies. 2016;2(4):142-5. doi: 10.19072/ijet.280563.
  23. Hajiloo M, Damavandi B, Hooshsadat M, Sangi F, et al. Breast cancer prediction using genome wide single nucleotide polymorphism data. BMC Bioinformatics. 2013;14(Suppl 13):S3. doi: 10.1186/1471-2105-14-S13-S3. PubMed PMID: 24266904. PubMed PMCID: PMC3891310.
  24. Brédart A, Kop JL, Antoniou AC, Cunningham AP, De Pauw A, et al. Clinicians’ use of breast cancer risk assessment tools according to their perceived importance of breast cancer risk factors: an international survey. J Community Genet. 2019;10(1):61-71. doi: 10.1007/s12687-018-0362-8. PubMed PMID: 29508368. PubMed PMCID: PMC6325038.
  25. Hou C, Zhong X, He P, Xu B, Diao S, Yi F, Zheng H, Li J. Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development. JMIR Med Inform. 2020;8(6):e17364. doi: 10.2196/17364. PubMed PMID: 32510459. PubMed PMCID: PMC7308891.
  26. Maas P, Barrdahl M, Joshi AD, Auer PL, et al. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncology. 2016;2(10):1295-302. doi: 10.1001/jamaoncol.2016.1025.
  27. Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. Radiology. 2019;292(1):60-6. doi: 10.1148/radiol.2019182716. PubMed PMID: 31063083.
  28. Koopmann BDM, Harinck F, Kroep S, Konings ICAW, Naber SK, et al. Identifying key factors for the effectiveness of pancreatic cancer screening: A model-based analysis. Int J Cancer. 2021;149(2):337-46. doi: 10.1002/ijc.33540. PubMed PMID: 33644856. PubMed PMCID: PMC8251934.
  29. Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127-57. doi: 10.3322/caac.21552. PubMed PMID: 30720861. PubMed PMCID: PMC6403009.
  30. Arefan D, Mohamed AA, Berg WA, Zuley ML, Sumkin JH, Wu S. Deep learning modeling using normal mammograms for predicting breast cancer risk. Med Phys. 2020;47(1):110-8. doi: 10.1002/mp.13886. PubMed PMID: 31667873. PubMed PMCID: PMC6980268.
  31. Yanes T, Young MA, Meiser B, James PA. Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field. Breast Cancer Res. 2020;22(1):21. doi: 10.1186/s13058-020-01260-3. PubMed PMID: 32066492. PubMed PMCID: PMC7026946.
  32. Feld SI, Woo KM, Alexandridis R, Wu Y, Liu J, et al. Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants. AMIA Annu Symp Proc. 2018;2018:1253-62. PubMed PMID: 30815167. PubMed PMCID: PMC6371301.
  33. Behravan H, Hartikainen JM, Tengström M, Kosma VM, Mannermaa A. Predicting breast cancer risk using interacting genetic and demographic factors and machine learning. Sci Rep. 2020;10(1):11044. doi: 10.1038/s41598-020-66907-9. PubMed PMID: 32632202. PubMed PMCID: PMC7338351.
  34. Dai B, Chen RC, Zhu SZ, Zhang WW. Using random forest algorithm for breast cancer diagnosis. 2018 International Symposium on Computer, Consumer and Control (IS3C); Taichung, Taiwan: IEEE; 2018. p. 449-52. doi: 10.1109/IS3C.2018.00119.
  35. He T, Puppala M, Ogunti R, Mancuso JJ, Yu X, Chen S, Chang JC, Patel TA, Wong ST. Deep learning analytics for diagnostic support of breast cancer disease management. 2017 IEEE EMBS international conference on biomedical & health informatics (BHI); Orlando, FL, USA: IEEE; 2017. p. 365-8. doi: 10.1109/BHI.2017.7897281.
  36. Shrivastav LK, Jha SK. A gradient boosting machine learning approach in modeling the impact of temperature and humidity on the transmission rate of COVID-19 in India. Appl Intell (Dordr). 2021;51(5):2727-39. doi: 10.1007/s10489-020-01997-6. PubMed PMID: 34764559. PubMed PMCID: PMC7609380.
  37. Xenochristou M, Hutton C, Hofman J, Kapelan Z. Water demand forecasting accuracy and influencing factors at different spatial scales using a Gradient Boosting Machine. Water Resources Research. 2020;56(8):e2019WR026304. doi: 10.1029/2019WR026304.
  38. Wang S, Wang Y, Wang D, Yin Y, Wang Y, Jin Y. An improved random forest-based rule extraction method for breast cancer diagnosis. Applied Soft Computing. 2020;86:105941. doi: 10.1016/j.asoc.2019.105941.
  39. Verma D, Mishra N. Analysis and prediction of breast cancer and diabetes disease datasets using data mining classification techniques. 2017 International Conference on Intelligent Sustainable Systems (ICISS); Palladam, India: IEEE; 2017. p. 533-8. doi: 10.1109/ISS1.2017.8389229.
  40. Janghel RR, Shukla A, Tiwari R, Kala R. Breast cancer diagnosis using artificial neural network models. The 3rd International Conference on Information Sciences and Interaction Sciences; Chengdu, China: IEEE; 2010. p. 89-94. doi: 10.1109/ICICIS.2010.5534716.
  41. Venkatesan E, Velmurugan T. Performance analysis of decision tree algorithms for breast cancer classification. Indian Journal of Science and Technology. 2015;8(29):1-8. doi: 10.17485/ijst/2015/v8i29/84646.
  42. Devarriya D, Gulati C, Mansharamani V, Sakalle A, Bhardwaj A. Unbalanced breast cancer data classification using novel fitness functions in genetic programming. Expert Systems with Applications. 2020;140:112866. doi: 10.1016/j.eswa.2019.112866.
  43. Chiesa M, Maioli G, Colombo GI, Piacentini L. GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets. BMC Bioinformatics. 2020;21(1):54. doi: 10.1186/s12859-020-3400-6. PubMed PMID: 32046651. PubMed PMCID: PMC7014945.
  44. Kim G, Kim S, Turbo Tek SK. Feature selection using genetic algorithms for handwritten character recognition. Proceedings of the Seventh International Workshop on Frontiers in Handwriting Recognition; Amsterdam, Nijmegen: International Unipen Foundation; 2002. p. 103-12.
  45. Guo J, White J, Wang G, Li J, Wang Y. A genetic algorithm for optimized feature selection with resource constraints in software product lines. Journal of Systems and Software. 2011;84(12):2208-21.
  46. Lavanya D, Rani KU. Ensemble decision tree classifier for breast cancer data. International Journal of Information Technology Convergence and Services. 2012;2(1):17-24.
  47. Rosner B, Tamimi RM, Kraft P, Gao C, et al. Simplified Breast Risk Tool Integrating Questionnaire Risk Factors, Mammographic Density, and Polygenic Risk Score: Development and Validation. Cancer Epidemiol Biomarkers Prev. 2021;30(4):600-7. doi: 10.1158/1055-9965.EPI-20-0900. PubMed PMID: 33277321. PubMed PMCID: PMC8026588.
  48. Tyrer J, Duffy SW, Cuzick J. A breast cancer prediction model incorporating familial and personal risk factors. Stat Med. 2004;23(7):1111-30. doi: 10.1002/sim.1668. PubMed PMID: 15057881.
  49. Conant EF, Barlow WE, Herschorn SD, Weaver DL, Beaber EF, et al. Association of Digital Breast Tomosynthesis vs Digital Mammography With Cancer Detection and Recall Rates by Age and Breast Density. JAMA Oncol. 2019;5(5):635-42. doi: 10.1001/jamaoncol.2018.7078. PubMed PMID: 30816931. PubMed PMCID: PMC6512257.
  50. Chow S, Raine-Bennett T, Samant ND, Postlethwaite DA, Holzapfel M. Breast cancer risk after hysterectomy with and without salpingo-oophorectomy for benign indications. Am J Obstet Gynecol. 2020;223(6):900.e1-7. doi: 10.1016/j.ajog.2020.06.040. PubMed PMID: 32585221.
  51. Raiesdana S. Breast Cancer Detection Using Optimization-Based Feature Pruning and Classification Algorithms. Middle East Journal of Cancer. 2021;12(1):48-68. doi: 10.30476/MEJC.2020.85601.1294.
  52. Mohan S, Bhattacharya S, Kaluri R, Feng G, Tariq U. Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting. International Journal of Distributed Sensor Networks. 2020;16(11). doi: 10.1177/1550147720971505.
  53. Sakri SB, Rashid NB, Zain ZM. Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access. 2018;6:29637-47. doi: 10.1109/ACCESS.2018.2843443.
  54. Kumar K, Singh VV, Ramaswamy R. Different Perspective of Machine Learning Technique to Better Predict Breast Cancer Survival. BioRxiv. 2020. doi: 10.1101/2020.07.03.186890.
  55. Thawkar S, Ingolikar R. Classification of masses in digital mammograms using the genetic ensemble method. Journal of Intelligent Systems. 2020;29(1):831-45. doi: 10.1515/jisys-2018-0091.
  56. Bayrak EA, Kırcı P, Ensari T. Comparison of machine learning methods for breast cancer diagnosis. 2019 Scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT); Istanbul, Turkey: IEEE; 2019. p. 1-3. doi: 10.1109/EBBT.2019.8741990.
  57. Alghunaim S, Al-Baity HH. On the scalability of machine-learning algorithms for breast cancer prediction in big data context. IEEE Access. 2019;7:91535-46. doi: 10.1109/ACCESS.2019.2927080.
  58. Kumar GR, Ramachandra GA, Nagamani K. An efficient prediction of breast cancer data using data mining techniques. International Journal of Innovations in Engineering and Technology (IJIET). 2013;2(4):139.
  59. Aruna S, Rajagopalan SP. A novel SVM based CSSFFS feature selection algorithm for detecting breast cancer. International Journal of Computer Applications. 2011;31(8):14-20.
  60. Memon MH, Li JP, Haq AU, Memon MH, Zhou W. Breast cancer detection in the IOT health environment using modified recursive feature selection. Wireless Communications and Mobile Computing. 2019;2019:1-19. doi: 10.1155/2019/5176705.
  61. Sun YS, Zhao Z, Yang ZN, Xu F, Lu HJ, et al. Risk Factors and Preventions of Breast Cancer. Int J Biol Sci. 2017;13(11):1387. doi: 10.7150/ijbs.21635. PubMed PMID: 29209143. PubMed PMCID: PMC5715522.
  62. Asri H, Mousannif H, Al Moatassime H, Noel T. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Computer Science. 2016;83:1064-9. doi: 10.1016/j.procs.2016.04.224.
  63. Ayvaci MU, Alagoz O, Chhatwal J, Munoz del Rio A, Sickles EA, Nassif H, Kerlikowske K, Burnside ES. Predicting invasive breast cancer versus DCIS in different age groups. BMC Cancer. 2014;14:584. doi: 10.1186/1471-2407-14-584. PubMed PMID: 25112586. PubMed PMCID: PMC4138370.
  64. Rajendran K, Jayabalan M, Thiruchelvam V. Predicting breast cancer via supervised machine learning methods on class imbalanced data. Int J Adv Comput Sci Appl. 2020;11(8):54-63. doi: 10.14569/IJACSA.2020.0110808.
  65. Atashi A, Sohrabi S, Dadashi A. Applying two computational classification methods to predict the risk of breast cancer: A comparative study. Multidisciplinary Cancer Investigation. 2018;2(2):8-13. doi: 10.30699/acadpub.mci.2.2.8.
  66. Mosayebi A, Mojaradi B, Bonyadi Naeini A, Khodadad Hosseini SH. Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer. PLoS One. 2020;15(10):e0237658. doi: 10.1371/journal.pone.0237658. PubMed PMID: 33057328. PubMed PMCID: PMC7561198.
  67. Jalali SM, Moro S, Mahmoudi MR, Ghaffary KA, Maleki M, Alidoostan A. A comparative analysis of classifiers in cancer prediction using multiple data mining techniques. International Journal of Business Intelligence and Systems Engineering. 2017;1(2):166-78. doi: 10.1504/IJBISE.2017.10009655.
  68. Lotfnezhad Afshar H, Jabbari N, Khalkhali HR, Esnaashari O. Prediction of Breast Cancer Survival by Machine Learning Methods: An Application of Multiple Imputation. Iran J Public Health. 2021;50(3):598-605. doi: 10.18502/ijph.v50i3.5606. PubMed PMID: 34178808. PubMed PMCID: PMC8214598.
  69. Nourelahi M, Zamani A, Talei A, Tahmasebi S. A model to predict breast cancer survivability using logistic regression. Middle East Journal of Cancer. 2019;10(2):132-8. doi: 10.30476/MEJC.2019.78569.
  70. Tapak L, Shirmohammadi-Khorram N, Amini P, Alafchi B, Hamidi O, Poorolajal J. Prediction of survival and metastasis in breast cancer patients using machine learning classifiers. Clinical Epidemiology and Global Health. 2019;7(3):293-9. doi: 10.1016/j.cegh.2018.10.003.
آمار
تعداد مشاهده مقاله: 4,289
تعداد دریافت فایل اصل مقاله: 2,150


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب