Volume 9, Issue 3 (2021)                   Health Educ Health Promot 2021, 9(3): 279-285 | Back to browse issues page

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Ayyoubzadeh S, Almasizand A, Rostam Niakan Kalhori S, Baniasadi T, Abbasi S. Early Breast Cancer Prediction Using Dermatoglyphics: Data Mining Pilot Study in a General Hospital in Iran. Health Educ Health Promot 2021; 9 (3) :279-285
URL: http://hehp.modares.ac.ir/article-5-53673-en.html
1- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
2- Department of Laboratory Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3- Department of Health Information Technology, Faculty of Para-Medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
4- Department of Laboratory Science, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran , sakineh4612004@yahoo.com
Abstract:   (1228 Views)
Aims: Dermatoglyphic is the study of skin patterns on hands and feet. It has been shown in some studies that specific finger patterns could be a risk factor for breast cancer. Thus, this study aims to evaluate fingerprint patterns and other easy-to-obtain features in the risk of breast cancer.
Instrument & Methods: This descriptive study was conducted in 2020. A dataset containing 462 records included female patients in Imam Khomeini Hospital Complex, Tehran, Iran. The factors' weight was determined by the Information Gain index. Predictive models were built once without fingerprint features and once with fingerprint features using Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine, and Deep Learning classifiers. RapidMiner 9.7.1 Software was used.
Findings: The most important factor determining breast cancer were age, having a child, menopause situation, and menopause age. The best performance was the Random Forest model with accuracy and Area under Curve of a Receiver operating characteristic of 84.43% and 0.923, respectively. The fingerprint patterns feature increased the RF accuracy from 79.44% to 84.43%.
Conclusion: An early breast cancer screening model could be built with the use of data mining methods. The fingerprint patterns could increase the performance of these models. The Random Forest model could be used. The results of such models could be used in designing apps for self-screening breast cancer.
Full-Text [PDF 576 kb]   (737 Downloads) |   |   Full-Text (HTML)  (611 Views)  
Article Type: Descriptive & Survey | Subject: Technology of Health Education
Received: 2021/05/29 | Accepted: 2021/08/29 | Published: 2021/10/17
* Corresponding Author Address: Department of Laboratory Science, 1st Floor, School of Allied Medical Sciences, Tehran University of Medical Sciences, No 17, Farredanesh Alley, Ghods St, Enghelab Ave, Tehran, Iran

References
1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394-424. [Link] [DOI:10.3322/caac.21492] [PMID]
2. Ferlay J, Colombet M, Soerjomataram I, Mathers C, Parkin D, Pineros M, et al. Estimating the global cancer incidence and mortality in 2018: Globocan sources and methods. Int J Cancer. 2019;144(8):1941-53. [Link] [DOI:10.1002/ijc.31937] [PMID]
3. Prathap L. Association of quantitative and qualitative dermatoglyphic variable and DNA polymorphism in female breast cancer population. Online J Health Allied Sci. 2017;16(2):1-5. [Link]
4. Wang L. Early diagnosis of breast cancer. Sensors. 2017;17(7):1572. [Link] [DOI:10.3390/s17071572] [PMID] [PMCID]
5. Haggag AA Khalil AMI, Elzahed ES, Abdelhamid MI. Study of fingerprints pattern in breast cancer patients insharkia governorate, a case-control retrospective clinical study. Zagazig Univ Med J. 2018;24(1):66-71. [Link] [DOI:10.21608/zumj.2018.13006]
6. Bleyer A, Welch HG. Effect of three decades of screening mammography on breast-cancer incidence. N Engl J Med. 2012;367(21):1998-2005. [Link] [DOI:10.1056/NEJMoa1206809] [PMID]
7. Autier P, Boniol M. Mammography screening: A major issue in medicine. Eur J Cancer. 2018;90:34-62. [Link] [DOI:10.1016/j.ejca.2017.11.002] [PMID]
8. Alexandraki I, Mooradian AD. Barriers related to mammography use for breast cancer screening among minority women. J Natl Med Assoc. 2010;102(3):206-18. [Link] [DOI:10.1016/S0027-9684(15)30527-7]
9. Stoll CRT, Roberts S, Cheng MR, Crayton EV, Jackson S, Politi MC. Barriers to mammography among inadequately screened women. Health Educ Behav. 2015;42(1):8-15. [Link] [DOI:10.1177/1090198114529589] [PMID]
10. Hassoun Y, Dbouk H, Saad Aldin E, Nasser Z, Abou abbas L, Nahleh Z, et al. Barriers to mammography screening: how to overcome them. Middle East J Cancer. 2015;6(4):243-51. [Link]
11. Azami-Aghdash S, Ghojazadeh M, Gareh Sheyklo S, Daemi A, Kolahdouzan K, Mohseni M, et al. Breast cancer screening barriers from the womans perspective: A meta-synthesis. Asian Pac J Cancer Prev. 2015;16(8):3463-71. [Link] [DOI:10.7314/APJCP.2015.16.8.3463] [PMID]
12. Shirzadi S, Allahverdipour H, Sharma M, Hasankhani H. Perceived barriers to mammography adoption among women in iran: A qualitative study. Korean J Acad Fam Med. 2020;41(1):20-7. [Link] [DOI:10.4082/kjfm.18.0054] [PMID] [PMCID]
13. Fayanju OM, Kraenzle S, Drake BF, Oka M, Goodman MS. Perceived barriers to mammography among underserved women in a breast health center outreach program. Am J Surg. 2014;208(3):425-34. [Link] [DOI:10.1016/j.amjsurg.2014.03.005] [PMID] [PMCID]
14. Tan MM, Ho WK, Yoon SY, Mariapun S, Hasan SN, Lee DS, et al. A case-control study of breast cancer risk factors in 7,663 women in Malaysia. Plos One. 2018;13(9):0203469. [Link] [DOI:10.1371/journal.pone.0203469] [PMID] [PMCID]
15. Metovic A, Musanovic J, Alicelebic S, Pepic E, Sljuka S, Mulic M. Predictive analysis of palmar dermatoglyphics in patients with breast cancer for small Bosnian-Herzegovinian population. Med Arch. 2018;72(5):357-61. [Link] [DOI:10.5455/medarh.2018.72.357-361] [PMID] [PMCID]
16. Abbasi S, Einollahi N, Dashti N, Vaez-Zadeh F. Study of dermatoglyphic patterns of hands in women with breast cancer. Pak J Medical Sci. 2006;22(1):18-22. [Link]
17. Chintamani, Khandelwal R, Mittal A, Saijanani S, Tuteja A, Bansal A, et al. Qualitative and quantitative dermatoglyphic traits in patients with breast cancer: A prospective clinical study. BMC Cancer. 2007;7:44. [Link] [DOI:10.1186/1471-2407-7-44] [PMID] [PMCID]
18. Sariri E, Kashanian M, Vahdat M, Yari S. Comparison of the dermatoglyphic characteristics of women with and without breast cancer. Eur J Obstet Gynecol Reprod Biol. 2012;160(2):201-4. [Link] [DOI:10.1016/j.ejogrb.2011.11.001] [PMID]
19. Mukherjee A, Madhushree KN, Nawab A, Ravindra S, Vivekananda MR, Shivaprasad D. Dermatoglyphics: A plausible risk indicator. Indian J Forensic Odontol. 2018;11(1):9-12. [Link] [DOI:10.21088/ijfo.0974.505X.11118.2]
20. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577:89-94. [Link] [DOI:10.1038/s41586-019-1799-6] [PMID]
21. Rajesh K, Anand S. Analysis of SEER dataset for breast cancer diagnosis using C4. 5 classification algorithm. Int J Adv Res Comput Commun Eng. 2012;1(2):72-7. [Link]
22. Chen TC, Hsu TC. A GAs based approach for mining breast cancer pattern. Expert Syst Appl. 2006;30(4):674-81. [Link] [DOI:10.1016/j.eswa.2005.07.013]
23. Senturk ZK, Kara R. Breast cancer diagnosis via data mining: Performance analysis of seven different algorithms. Comput Sci Eng Int J. 2014;4(1):35-46. [Link] [DOI:10.5121/cseij.2014.4104]
24. Padmavati J. A comparative study on breast cancer prediction using RBF and MLP. Int J Sci Eng Res. 2011;2(1):1-5. [Link]
25. Kuo WJ, Chang RF, Chen DR, Lee CC. Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast Cancer Res Treat. 2001;66(1):51-7. [Link] [DOI:10.1023/A:1010676701382] [PMID]
26. Delen D, Walker G, Kadam A. Predicting breast cancer survivability: A comparison of three data mining methods. Artif Intell Med. 2005;34(2):113-27. [Link] [DOI:10.1016/j.artmed.2004.07.002] [PMID]
27. Alshammari SM, Shah TM, Huang Y. Data mining techniques for predicting breast cancer survivability among women in the United States [Internet]. Denton: University of North Texas Libraries; 2014 [cited 2021 Aug 21]. Available from: https://digital.library.unt.edu/ark:/67531/metadc277303/. [Link]
28. Aruna S, Rajagopalan S, Nandakishore L. Knowledge based analysis of various statistical tools in detecting breast cancer. Comput Sci Inf Technol. 2011;2:37-45. [Link]
29. archive.ics.uci.edu [Internet]. Irvine: UCI Machine Learning Repository; 2017 [cited 2021 Aug 21]. Available from: https://archive.ics.uci.edu/ml/index.php. [Link]
30. https://www.sciencedirect.com/science/article/pii/B9780123814791000083?via%3Dihub [Link]
31. Zwitte M, Soklic M. Breast cancer data set [Internet]. Irvine: UCI Machine Learning Repository; 1988 [cited 2021 Aug 21]. Available from: https://archive.ics.uci.edu/ml/datasets/breast+cancer. [Link]
32. Han J, Pei J, Kamber M. Data mining: concepts and techniques. 3rd Edition. Amsterdam: Elsevier; 2011. [Link]
33. Patil TR, Sherekar SS. Performance analysis of Naive Bayes and J48 classification algorithm for data classification. Int J Comput Sci Appl. 2013;6(2):256-61. [Link]
34. Gupta B, Rawat A, Jain A, Arora A, Dhami N. Analysis of various decision tree algorithms for classification in data mining. Int J Comput Appl. 2017;163(8):15-9. [Link] [DOI:10.5120/ijca2017913660]
35. Krauss C, Do XA, Huck N. Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. Eur J Oper Res. 2017;259(2):689-702. [Link] [DOI:10.1016/j.ejor.2016.10.031]
36. Chuang MT, Hu Yh, Lo CL. Predicting the prolonged length of stay of general surgery patients: A supervised learning approach. Int Trans Oper Res. 2018;25(1):75-90. [Link] [DOI:10.1111/itor.12298]
37. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: Review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236-46. [Link] [DOI:10.1093/bib/bbx044] [PMID] [PMCID]
38. Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-44. [Link] [DOI:10.1038/nature14539] [PMID]
39. Jung Y, Hu J. A K-fold averaging cross-validation procedure. J Nonparametr Stat. 2015;27(2):167-79. [Link] [DOI:10.1080/10485252.2015.1010532] [PMID] [PMCID]
40. Al-Quraishi T, Abawajy JH, Chowdhury MU, Rajasegarar S, Abdalrada AS. Breast cancer recurrence prediction using random forest model. In: Ghazali R, Deris M, Nawi N, Abawajy J. Advances in intelligent systems and computing. Cham: Springer; 2018. [Link] [DOI:10.1007/978-3-319-72550-5_31]
41. Murugan S, Kumar BM, Amudha S, editors. Classification and prediction of breast cancer using linear regression, decision tree and random forest. 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC); 8-9 Sept 2017, Mysore, India. Picataway: IEEE; 2017. [Link] [DOI:10.1109/CTCEEC.2017.8455058]
42. Nguyen C, Wang Y, Nguyen HN. Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic. J Biomed Sci Eng. 2013;6(5):551-60. [Link] [DOI:10.4236/jbise.2013.65070]
43. 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. [Link] [DOI:10.1148/radiol.2019182716] [PMID]
44. Khan S, Islam N, Jan Z, Ud Din I, Rodrigues JJPC. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett. 2019;125:1-6. [Link] [DOI:10.1016/j.patrec.2019.03.022]
45. Akselrod-Ballin A, Chorev M, Shoshan Y, Spiro A, Hazan A, Melamed R, et al. Predicting breast cancer by applying deep learning to linked health records and mammograms. Radiology. 2019;292(2):331-42. [Link] [DOI:10.1148/radiol.2019182622] [PMID]
46. Venkatesan E, Velmurugan T. Performance analysis of decision tree algorithms for breast cancer classification. Indian J Sci Technol. 2015;8(29):1-8. [Link] [DOI:10.17485/ijst/2015/v8i29/84646]
47. Sani FQ, Mirfat M, Iskandar I. Dermatoglyphics pattern on breast cancer patients in dharmais cancer hospital. Glob Med Health Commun. 2020;8(1):47-52. [Link] [DOI:10.29313/gmhc.v8i1.4470]
48. Musanovic J, Metovic A, Pepic E, Kapic D, Cosovic E, Rebic D, et al. Predictive values of quantitative analysis of finger and palmar dermatoglyphics in patients with breast cancer for Bosnian-Herzegovinian population. J Evolution Med Dent Sci. 2018;7(24):2855-60. [Link] [DOI:10.14260/jemds/2018/644]
49. Manhas S, Singh U. Clinical significance of dermatoglyphics in patients with breast cancer-A review article. Int J Inf Comput Sci. 2018;5(8):152-6.

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