An Intelligent Fuzzy Model for Managing Uncertainty in Diagnosis of the Breast Cancer Staging | ||
| Engineering Management and Soft Computing | ||
| مقاله 3، دوره 8، شماره 2 - شماره پیاپی 15، مهر 2022، صفحه 29-43 اصل مقاله (928.87 K) | ||
| نوع مقاله: Original Article | ||
| نویسندگان | ||
| Leila Honari mahmod1؛ Rahil Hosseini* 2؛ Mahd Mazinani3 | ||
| 1MSc. Faculty of Engineering, Department of Computer Engineering, Islamic Azad University, Tehran, Iran. Email: rahil.hosseini@qodsiau.ac.ir | ||
| 2Assistant Prof., Faculty of Engineering, Department of Computer Engineering, Islamic Azad University, Tehran, Iran. Email: leilahonari@gmail.com | ||
| 3Assistant Prof., Faculty of Engineering, Department of Electrical Engineering, Islamic Azad University, Tehran, Iran. Email: Mahdi.mazinani@qodsiau.ac.ir | ||
| چکیده | ||
| Intelligent assistant systems have been focused in many researches for managing uncertainties in medical diagnosis in the recent years. Due to vagueness in diagnosis of Breast cancer stages which is one of the major cause of death in women, early diagnosis of stages of this cancer can help physicians to choose the best treatment option. In this research, an intelligent Model based on fuzzy logic has been presented to manage uncertainty associated with input and outputs in the diagnosis of stages of breast cancer. This model implements human experience with membership functions and fuzzy rules and is a general method for combining knowledge, intelligent technology, control and decision making. In this study, the medical records of 400 patients with breast cancer with 3 features were studied and their results were evaluated by a group of experts. The results of the system efficiency were investigated using an ROC curve analysis method. The specificity, sensitivity and accuracy in steps 1, 4 of the Breast cancer were (97/50%, 98/43% ), in order. | ||
| کلیدواژهها | ||
| Breast cancer؛ fuzzy logic؛ Mamdani fuzzy inference system؛ Modelling uncertainty | ||
| عنوان مقاله [English] | ||
| -ارایه یک مدل هوشمند فازی در مدیریت عدم قطعیت در تشخیص مرحله پیشرفت سرطان سینه | ||
| نویسندگان [English] | ||
| لیلا هنری محمود1؛ راحیل حسینی2؛ مهدی مزینانی3 | ||
| 1کارشناسی ارشد هوش مصنوعی، گروه مهندسی کامپیوتر، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران. رایانامه: leilahonari@gmail.com | ||
| 2استادیار، گروه مهندسی کامپیوتر، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران. رایانامه: rahil.hosseini@qodsiau.ac.ir | ||
| 3استادیار، گروه مهندسی برق-الکترونیک، واحد شهر قدس، دانشگاه آزاد اسلامی، تهران، ایران. رایانامه: Mahdi.mazinani@qodsiau.ac.ir | ||
| چکیده [English] | ||
| در سال های اخیر سسیتم های هوشمند تصمیم یار موردتوجه پژوهش های فراوانی جهت مدیریت عدم قطعیت در تشخیص های پزشکی قرار گرفته است. باتوجه به ابهام در تشخیص مراحل سرطان پستان که یکی از دلایل عمده و اصلی مرگ ومیر زنان در دهه اخیر بوده است، تشخیص زودهنگام مرحله پیشرفت سرطان می تواند شانس بهبودی کامل را افزایش دهد تا بهترین گزینه درمان انتخاب شود. در این پژوهش مدلی جهت مدیریت عدم قطعیت مبتنی بر منطق فازی با امکان مدیریت عدم قطعیت در ورودیها و خروجیها در مرحله بندی سرطان سینه، ارائه میشود. در این مطالعه، پرونده پزشکی400بیمار مبتلابه سرطان پستان با تعداد 3 ویژگی مورد بررسی قرارگرفته ونتایج آن توسط گروهی از متخصصان خبره موردبررسی قرارگرفته است. نتایج بهدستآمده کارایی سیستم با استفاده از روش تحلیل منحنی ROC مورد بررسی قرار گرفت که میزان صحت عملکرد به ترتیب برای مراحل 1و4 سرطان سینه97/50 درصد،و98/46 درصد از جنبه معیار سطح زیر منحنی بدست آمده است. | ||
| کلیدواژهها [English] | ||
| مرحله پیشرفت سرطان سینه, منطق فازی, سیستم استنتاج فازی ممدانی, مدل سازی عدم قطعیت | ||
| مراجع | ||
|
Abadeh MS, Habibi J, Soroush E. Induction of Fuzzy Classification Systems via Evolutionary ACO- Based Algorithms. International Journal of Simulation, Systems, Science, Technology, 2008; 9:1- 8. DOI: https://doi.org/10.1109/AMS.2007.53 American Cancer Society. Breast cancer facts& figures 2009-2010. [cited 2006 Feb 11].Available from: http://www.cancer.org Amin, M.B. Edge, S. Greene, F. Byrd, D.R. Brookland, R.K.Washington, M.K. Gershenwald, J.E. Compton, C.C. Hess, K.R. Sullivan, D.C. Jessup, J.M. Brierley, J.D. Gaspar, L.E. Schilsky, R.L. Balch, C.M. Winchester, D.P. Asare, E.A. Madera, M. Gress, D.M. Meyer, L.R. (Eds).2018. 8 edition (September 5, 2018) AJCC Cancer Staging Manual. American College of Surgeons, 1032. DOI: https://doi.org/10.1089/thy.2017.0102 Asadabadi A, Bahrampour A, Haghdoost A. Prediction of Breast Cancer Survival by Logistic Regression and Artificial Neural Network Models. irje. 2014; 10 (3):1-8 DOI: https://doi.org/10.32996/jmss Askarpour, Somayeh and Seyed Hamid Ghafouri, 2015, Investigation of Data Mining Algorithms in Breast Cancer Diagnosis, 3rd Electronic Conference on New Research in Science and Technology, Electronically, Alam-e-Mehran Aseman Co. DOI: https://doi.org/10.5812/semj-120140 Cancer: Interdisciplinary InternationalJournal of the American Cancer Society1997; 80(9):1803-4 Dehghan P, Mogharabi M, Zabbah I, Layeghi K, Maroosi A. Modeling Breast Cancer Using Data Mining Methods. Journal of Health and Biomedical Informatics. 2018; 4 (4):266-278. Delen, D. Walker, G. & Kadam, A. (2005). Predicting breast cancer survivability: a comparison of three data mining methods. Artificial intelligence in medicine, 34(2), 113-127 DOI: https://doi.org/10.1016/j.artmed.2004.07.002 Faraggi D, Reiser B. Estimating of area under the ROCcurve. Stat Med. 2002; 21:3093-3106 DOI: https://doi.org/10.1002/sim.1228 Ganji MF, Abadeh MS. An Intelligence Fuzzy Classification System for Diabetes Detection. Iranian Conference on Fuzzy Systems, 2010. DOI: https://doi.org/10.22044/jadm.2023.12383.2383 Ganji MF, Abadeh MS. Parallel Fuzzy Rule Learning Using an ACO-Based Algorithm for Medical Data Mining. IEEE Fifth International Conference on Bio-Innspired Compting: theories and Applications, 2010: 573-581 DOI: https://doi.org/10.1109/BICTA.2010.5645189 Ganji MF, Abadeh MS. Using Fuzzy Ant Colony Optimization for Diagnosis of Diabetes Disease. Iranian conference of Electrical Engineering, ICEE, 2011. DOI: https://doi.org/10.1109/IRANIANCEE.2010.5507019 Ghasem Ahmad Leila. Prediction of recurrence of breast cancer using three data mining techniques. Iranian Journal of Breast Diseases. 2012; 5 (4): 23-34. DOI: https://doi.org/10.4172/2157-7420.1000124 Hanley JA, McNeil BJ. The meaning and use of the areaunder a receiver operating characteristic (ROC) curve.Radiology. 1982;143:29-36 DOI: https://doi.org/10.1148/radiology.143.1.7063747 Hortobagyi GN, de la Garza Salazar J,Pritchard K, Amadori D, Haidinger R, HudisCA, et al. The global breast cancer burden: variations in epidemiology and survival.Clin Breast Cancer. 2005; 6(5):391- 401. DOI: https://doi.org/10.3816/cbc.2005.n.043 Hosseini, Raheel and Mehdi Mazinani, 2014, A Fuzzy Mamdani Inference System for Diagnosis of Breast Cancer in Intelligent Computer System using Medical Diagnosis, National Conference on Computer Science and Engineering, Mashhad, Khavaran Institute of Higher Education. DOI: https://doi.org/10.1109/ICCIC.2015.7435670 Hosseini, Raheel and Mehdi Mazinani, 2014, Classification of Uncertainty Sources in Intelligent Medical Image Analysis and Processing Devices, National Conference on Computer Science and Engineering, Mashhad, Khavaran Institute of Higher Education. Jafari Souk, Alemi and Hamed Shahbazi, 2015, A Review of Fuzzy Inference Algorithms, 4th National Conference on New Ideas in Electrical Engineering, Isfahan, Islamic Azad University, Isfahan (Khorasgan) Dor: 10.30495/ijsee.2022.1969247.1232 Kenarkoohi A, soleimanjahi H, Falahi S, Riahi Madvar H, Meshkat Z. The application of the new intelligent Adaptive Nero Fuzzy Inference System (ANFIS) in prediction of human papilloma virus oncogenicity potency. J Arak Uni Med Sci. 2011; 13 (4):95-105. DOI: https://doi.org/10.1007/s11356-012-1027-5 Khanna R, Taneja V, Singh SK, Kumar N, Sreenivas V,Puliyel JM. The clinical risk index of babies (CRIB)score in India. Indian J Pediatr. 2002;69:957-60 DOI: https://doi.org/10.1007/BF02726013 Khezri, R. Hosseini, R. & Mazinani, M. (2014). A fuzzy rule-based expert system for the prognosis of the risk of development of the breast cancer 1557-1564. DOI: https://doi.org/10.5829/idosi.ije.2014.27.10a.09 Khosravanian A, Rahmanimanesh M, Keshavarzi P. Designing a Group Decision-Making System Using a Fuzzy Combination of Regression Methods for Prediction of Benign or Malignant Breast Tumors. ijbd. 2017; 10 (3):55-66 Dor: 20.1001.1.17359406.1396.10.3.6.9 Langarizadeh M, Mahmud R. Determination Of Breast Density Using Fuzzy Logic. payavard. 2014; 8 (3):210-219. DOI: https://doi.org/10.4102/sajr.v22i2.1358 Latif A M, Momeny M, Sarram R, Agha Srram M, Pour Ahmadi A, Haj Ebrahimi Z. Using Data Mining and Genetic Algorithm for Diagnosis of Breast Cancer. ijbd. 2016; 9 (1):45-56 Dor: 20.1001.1.17359406.1395.9.1.6.8 Lundin, M. Lundin, J. Burke, H. B. Toikkanen, S. Pylkkänen, L. & Joensuu, H. (1999). Artificial neural networks applied to survival prediction in breast cancer. Oncology, 57(4), 281-286. DOI: https://doi.org/10.1159/000012061 Mahdavi, Henganga and Hassan Rashidi, 2015, Diagnosis of breast cancer progression using adaptive neuro-fuzzy clustering and data mining methods, 3rd International Conference on Applied Research in Computer Engineering and Information Technology, Tehran, Malek Ashtar University of Technology DOI: https://doi.org/10.48301/kssa.2022.277156.1426 Nooshin Bigdeli, Hamed Jabbari, Negar Maleki, An Intelligent Hybrid Method for Detection, Demarcation and Classification of Breast Masses Based on The Characteristics of New Tissues Based on Two Images of Mammography, Machine Vision and Image Processing, Accepted, November 16, 2017 Dor: 20.1001.1.23831197.1397.5.2.5.0 Olfatbakhsh A. Haghighat S. Tabari MR. Hashemi E. Sari F. Kaviani A. Patient Satisfaction and Body Image Following Mastectomy, Breast-Conserving Therapy, and Mastectomy With Reconstruction: A Study in Iran”. Arch Breast Cancer, 2018. 5(4):173-182 DOI: https://doi.org/10.32768/abc.201854173-182 Padmapriya, B. & Velmurugan, T. (2014, December). A survey on breast cancer analysis using data mining techniques. In 2014 IEEE International Conference on Computational Intelligence and Computing Research (pp. 1-4). IEEE DOI: https://doi.org/10.1109/ICCIC.2014.7238530 Pendharkar, P. C. Rodger, J. A. Yaverbaum, G. J. Herman, N. & Benner, M. (1999). Association, statistical, mathematical and neural approaches for mining breast cancer patterns. Expert Systems with Applications, 17(3), 223-232. DOI: https://doi.org/10.1016/s0957-4174(99)00036-6 Sadeghnezhad F, NiknamiSh, Ghaffari M, Effect of health education methods on promoting breast self examination (BSE), Journal of Birjand University of MedicalSciences 2009; 15(4): 38-48. DOI: https://doi.org/10.4103/jehp.jehp_1119_20 Setayeshi S, Akbari ME, Darghahi R,Haghighat khah HR."Breast Cancer and Technical Analysis of its Diagnostics". Tehran:Bitarafan; 2011. Persian. DOI: https://doi.org/10.18502/ijph.v50i8.6802 Sobin LH, Fleming ID. TNM classification of malignant tumors, (1997). DOI: https://doi.org/10.1002/(sici)1097-0142(19971101)80:9<1803::aid-cncr16>3.0.co;2-9 Tsang CH, Kwong S, Wang H. Genetic-Fuzzy Rule Mining Approach and Evolution of Feature Selection Techniques for Anomaly Intrusion Detection. Pattern Recognition, 2009; 40: 2373-2391 DOI: https://doi.org/10.1016/j.patcog.2006.12.009 Yi, W. & Fuyong, W. (2006, August). Breast cancer diagnosis via supp ort vector machines. In Control Conference, 2006. CCC 2006. Chinese (pp. 1853-1856). IEEE. DOI: https://doi.org/10.1021/ci0256438 | ||
|
آمار تعداد مشاهده مقاله: 921 تعداد دریافت فایل اصل مقاله: 901 |
||