Skip to main content

Advertisement

SpringerLink
Log in

Expert system based on fuzzy rules for diagnosing breast cancer

  • Original Paper
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

Present problem of every country nowadays is not to defeat other countries in a war and protect its citizens, rather to defeat the deadly diseases in their own country, which are paralyzing the strength of the countries by their adverse effects. One of the major threatening diseases among them is cancer. According to latest reports given by WHO, nearly 2.3 million women were diagnosed by breast cancer in year 2020 which resulted in 685,000 deaths worldwide and this graph was highly ascended till the end of 2020 which reported 7.8 million women (alive) tested positive with breast cancer in the past 5 years which made the Breast cancer as the most prevalent cancer among all other cancers. Cancer is the result of changes in genes known as mutations, which are responsible for the growth of cells. Breast cancer cells typically form a tumor that can be diagnosed by either of these i.e., mammography, ultrasound, MRI, biopsy or by all which are dreadful and expensive procedures. Many expert systems are designed to help the oncologists in diagnosing the disease so as to save their time and efficiency, which can be utilized in treating the patients after diagnosis and gifting them back the crown of their life from which they were almost deprived off. Still there are some problems due to which these expert systems are not fully utilized for which they are meant. First problem is that these expert systems are limited to cities because of which villagers are deprived of this facility. As we know, even after 75 years of independence, women of village area are not given importance and so is their health. Second and most prevalent problem seen is the complicated and dreadful diagnosis procedure, which threatens women the most. To solve first problem, we have taken the decision to implement this system in villages after increasing its accuracy more, which will be convenient and easy for villagers. For making diagnosis phase comfortable for women, this research has opted the way which will not frighten the women, that is just to have a blood analysis by giving blood sample which can be easily undertaken without any panic, adding up a feather of novelty to our research. This work is surely going to help the oncologist, as the work has been done under the guidance of oncologist and rules of fuzzy logic are designed as per the information shared by oncologist. This paper intends to talk about the expert system that diagnosis the breast cancer using the values of nine parameters taken from Coimbra breast cancer dataset of UCI machine learning repository such as age, BMI, glucose, insulin, Homa-IR, leptin, adiponectin, Mcp-1 and resistin and gives the output as benign or malignant. This expert system is based on fuzzy logic rules which use mamdani interface system having fuzzifier for fuzzyfying the data values of parameters received from simple blood analysis, memory is used to store rules which predict the output and defuzzifier gives the output as benign or malignant with promising accuracy, sensitivity and specificity as 90.3%, 87.3% and 95% respectively. To our knowledge till now no such expert system is designed with all these attributes giving such high accuracy. It is easy to go process of diagnosing the disease with promising accuracy which will attract the women with symptoms to get it diagnosed at early stages thus help in reducing the mortality rates to a large fag end.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

All data and materials support the published claims and comply with field standards.

Code availability

Software application or custom code support the published claims and comply with field standards.

References

  1. Punitha S, Amuthan A, Joseph KS. Enhanced Monarchy Butterfly Optimization Technique for effective breast cancer diagnosis. Journal of Medical System. 2019;43:206. https://doi.org/10.1007/s10916-019-1348-8.

    Article  Google Scholar 

  2. Srinivasan D, Gopalakrishnan M. Breast Cancer Detection Using Adaptable Textile Antenna Design. Journal of Medical System. 2019;43:177. https://doi.org/10.1007/s10916-019-1314-5.

    Article  Google Scholar 

  3. Diz J, Marreiros G, Freitas A. Applying Data Mining Techniques to Improve Breast Cancer Diagnosis. Journal of Medical System. 2016;40:203. https://doi.org/10.1007/s10916-016-0561-y.

    Article  Google Scholar 

  4. Hernandez -Capistran J., Martínez-Carballido J.F., Rosas-Romero R. False Positive Reduction by an Annular Model as a Set of Few Features for Microcalcification Detection to Assist Early Diagnosis of Breast Cancer. Journal of Medical System. 2018;42:134. https://doi.org/10.1007/s10916-018-0989-3.

    Article  Google Scholar 

  5. https://www.who.int/news-room/fact-sheets/detail/breast-cancer.

  6. https://www.breastcancerindia.net/statistics/stat_global.html.

  7. Kadam VJ, Jadhav SM, Vijayakumar K. Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression. Journal of Medical System. 2019;43:263. https://doi.org/10.1007/s10916-019-1397-z.

    Article  Google Scholar 

  8. Issac NS, Palanisamy P, Chibbar R, et al. An Expert Support System for Breast Cancer Diagnosis using Color Wavelet Features. Journal of Medical System. 2012;36:3091–102. https://doi.org/10.1007/s10916-011-9788-9.

    Article  Google Scholar 

  9. Zheng B, Yoon SW, Lam SS. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert System Application. 2014;41(4):1476–82.

    Article  Google Scholar 

  10. Singh SP, Urooj S. An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier. Journal of Medical System. 2016;40:105. https://doi.org/10.1007/s10916-016-0454-0.

    Article  Google Scholar 

  11. Karabatak M, Ince MC. an expert system for detection of breast cancer based on association rules and neural network. Expert System Applications. 2009;36(2):3465–9.

    Article  Google Scholar 

  12. Ramya DR, Anandhamala GS. Analysis of Breast Thermograms Using Asymmetry in Infra-Mammary Curves. Journal of Medical System. 2019;43:146. https://doi.org/10.1007/s10916-019-1267-8.

    Article  Google Scholar 

  13. Eltoukhy MM, Faye I, Samir BB. Breast cancer diagnosis in digital mammogram using multiscale curvelet transform. Computerized medical imaging and graphics. Elsevier. 2010;34(4):269-76.

  14. Saritas I. Prediction of breast cancer using artificial neural networks. Journal of Medical System 2012;36:2901–2907. https://doi.org/10.1007/s10916-011-9768-0.

  15. Roy CS, Roy A, Saha H. ASIC Design of a Digital Fuzzy System on Chip for Medical Diagnostic Applications. Journal of Medical System. 2011;35:221–35. https://doi.org/10.1007/s10916-009-9359-5.

    Article  Google Scholar 

  16. Lahsasna A, Ainon RN, Zainuddin R, et al. Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis. Journal of Medical System. 2012;36:3293–306. https://doi.org/10.1007/s10916-012-9821-7.

    Article  Google Scholar 

  17. Ubeyli ED. Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast cancer. Journal of Medical System. 2009;33:353. https://doi.org/10.1007/s10916-008-9197-x.

    Article  Google Scholar 

  18. Kasbe T, Pippal RS. Design of heart disease diagnosis system using fuzzy logic, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). 2017;3183–3187.

  19. Thani I, Kasbe T. Breast Cancer: State –of- the-art causes and diagnosis, 2nd International Conference on Data. Eng Applications (IDEA). 2021;1–6.

  20. Badid N, Baba Ahmed FZ, Merzouk H. Oxidant/Antioxidant Status, Lipids and Hormonal Profile in Overweight Women with Breast Cancer. Pathology and Oncology Research. 2010;16:159–67.

    Article  Google Scholar 

  21. Crisostomo J, Matafome P, Santos-Silva D. Hyperresistinemia and metabolic dysregulation: a risky crosstalk in obese breast cancer. Endocrine. 2016;53:433–42.

    Article  Google Scholar 

  22. Saxena NK, Sharma D. Multifaceted Leptin Network: The Molecular Connection between Obesity and Breast Cancer. J Mammary Gland Biol Neoplasia. 2013;18:309–20.

    Article  Google Scholar 

  23. Cust AE, Stocks T, Lukanova A. the influence of overweight and insulin resistance on breast cancer risk and tumour stage at diagnosis: a prospective study. Breast Cancer Res Treat. 2009;113:567–76.

    Article  Google Scholar 

  24. Engin A. Obesity-associated Breast Cancer: Analysis of risk factors. Obesity and Lipotoxicity, Advances in Experimental Medicine and Biology. Springer. 2017;960:571–606.

    Google Scholar 

  25. Healy LA, Ryan AM, Carroll P, Ennis D, Crowley V, Boyle T, Kennedy MJ, Connolly E, Reynolds JV. Metabolic Syndrome, Central Obesity and Insulin Resistance are Associated with Adverse Pathological Features in Postmenopausal Breast Cancer. Clin Oncol. 2010;22(4):281–8.

    Article  Google Scholar 

  26. Ines B, Cinzia G, Daniela B, Sebastiano A, Stefania C. The weight of obesity in breast cancer progression and metastasis: Clinical and molecular perspectives. Seminars in Cancer Bio. 2020;60:74–284.

  27. Pichard C, Plu-Bureau G, Neves-e CM, Gompel A. Insulin resistance, obesity and breast cancer risk. Maturitas. 2008;60(1):19–30.

    Article  Google Scholar 

  28. Saxena NK, Sharma D. Multifaceted Leptin Network: The Molecular Connection between Obesity and Breast Cancer. J Mammary Gland Biology and Neoplasia. 2013;18:309–20. https://doi.org/10.1007/s10911-013-9308-2.

    Article  Google Scholar 

  29. Flores-Lopez LA, Martinez-Hernandez MG, Viedma-Rodríguez R. High glucose and insulin enhance uPA expression, ROS formation and invasiveness in breast cancer-derived cells. Cell Oncology. 2016;39:365–78. https://doi.org/10.1007/s13402-016-0282-8.

    Article  Google Scholar 

  30. Alokail MS, Al-Daghri NM, Al-Attas OS. Combined effects of obesity and type 2 diabetes contribute to increased breast cancer risk in premenopausal women. Cardiovasc Diabetol. 2009;8:33.

    Article  Google Scholar 

  31. Niraula S, Dowling RJO, Ennis M. Metformin in early breast cancer: a prospective window of opportunity neoadjuvant study. Breast Cancer Res Treat. 2012;135:821–30.

    Article  Google Scholar 

  32. Nyante SJ, Gammon MD, Kaufman JS. Common genetic variation in adiponectin, leptin, and leptin receptor and association with breast cancer subtypes. Breast Cancer Res and Treatment. 2011;129:593–606. https://doi.org/10.1007/s10549-011-1517-z.

  33. D’Esposito V, Passaretti F, Hammarstedt A. Adipocyte-released insulin-like growth factor-1 is regulated by glucose and fatty acids and controls breast cancer cell growth in vitro. Diabetologia. 2012;55:2811–22. https://doi.org/10.1007/s00125-012-2629-7.

    Article  Google Scholar 

  34. La Vecchia C, Giordano SH, Hortobagyi GN, Chabner B. Overweight, obesity, diabetes, and risk of breast cancer: interlocking pieces of the puzzle. Oncologist. 2011;16:726–9.

    Article  Google Scholar 

  35. Viedma-Rodríguez R, Martínez-Hernández MG, Flores-Lopez LA. Epsilon- aminocaproic acid prevents high glucose and insulin induced-invasiveness in MDA-MB- 231 breast cancer cells, modulating the plasminogen activator system. Mol Cell Biochem. 2018;437:65–80.

    Article  Google Scholar 

  36. Krishnan MMR, Banerjee S, Chakraborty C, Chakraborty C, Ray AK. Statistical analysis of mammographic features and its classification using support vector machine. Expert system application. 2010;37:470–8.

    Article  Google Scholar 

  37. Hsu J, Hung P, Lin H. Applying Under-Sampling Techniques and Cost- Sensitive Learning Methods on Risk Assessment of Breast Cancer. Journal of Medical System. 2015;39:40. https://doi.org/10.1007/s10916-015-0210-x.

    Article  Google Scholar 

  38. El Houby EMF. Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images. Journal of Medical System. 2018;42:157. https://doi.org/10.1007/s10916-018-1010-x.

    Article  Google Scholar 

  39. Neshat M, Yaghobi M, Naghibi MB, Esmaelzadeh A. Fuzzy Expert System Design for Diagnosis of Liver Disorders. 2008 International Symposium on Knowledge Acquisition and Modeling. Wuhan. 2008;2008:252–6.

    Google Scholar 

  40. Sayed S, Ahmed S, Poonia R. Holo entropy enabled decision tree classifier for breast cancer diagnosis using Wisconsin (prognostic) data set. 7th International Conference on Communication Systems and Network Technologies (CSNT), Nagpur. 2017;172–176.

  41. Maria DS, R.F., Jose de J. G. G., Jesus D.M., Guadalupe M.G., Angel G.V., Jhony De la C.V. Insulin and Leptin Levels in Obese Patients with and Without Breast Cancer. Clin Breast Cancer. 2013;13(6):482–5.

    Article  Google Scholar 

  42. Da Chung C, Yueh FC, Yao TY, Hso CC, Fu CK, Ou YF, Hue YC, Ming FH, Shyng SFY. Serum adiponectin and leptin levels in Taiwanese breast cancer patients. Cancer Lett. 2006;237(1):109–14.

    Article  Google Scholar 

  43. Grossmann ME, Ray A, Nkhata KJ. Obesity and breast cancer: status of leptin and adiponectin in pathological processes. Cancer Metastasis. 2010;29:641–53.

    Article  Google Scholar 

  44. Ray A. Adipokine leptin in obesity-related pathology of breast cancer. Journal of Bioscience. 2012;37:289–94.

    Article  Google Scholar 

  45. Micheal OA, Marion OA, Ayodele AA, et al. Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier. J Big Data. 2021;8:29. https://doi.org/10.1186/s40537-021-00415-z.

    Article  Google Scholar 

  46. Marion OA, Micheal OA, et al. A genetic algorithm for prediction of RNA-seq malaria vector gene expression data classification using SVM kernels. Bulletin of Electrical Eng Informatics. 2021;10(2):1071–1079. https://doi.org/10.11591/eei.v10i2.2769.

  47. Micheal OA, Marion OA, Ayodele AA. A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree. Telecomm Computing Electron Control. 2021;19(1):310–316. https://doi.org/10.12928/telkomnika.v19i1.16381.

  48. Micheal OA, Marion OA, Ayodele AA. An ICA-ensemble learning approaches for prediction of RNA-seq malaria vector gene expression data classification. Int J Electrical Comp Eng (IJECE). 2021;11(2):1561–69. https://doi.org/10.11591/ijece.v11i2.pp1561-1569.

  49. Patricio M, Pereira J, Crisostomo J, et al. Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer 2018;18(29). https://doi.org/10.1186/s12885-017-3877-1.

  50. https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Coimbra.

Download references

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Faculty of Computer Science, Research Scholar, Oriental University, Indore, Madhya Pradesh, India

    Isha Thani

  2. Associate Professor, Faculty of Computer Science, Oriental University, Madhya Pradesh, Indore, India

    Tanmay Kasbe

Authors
  1. Isha Thani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tanmay Kasbe
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

(optional—please review the submission guidelines from the journal whether statements are mandatory).

Corresponding author

Correspondence to Tanmay Kasbe.

Ethics declarations

Conflicts of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thani, I., Kasbe, T. Expert system based on fuzzy rules for diagnosing breast cancer. Health Technol. 12, 473–489 (2022). https://doi.org/10.1007/s12553-022-00643-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12553-022-00643-0

Keywords

Search

Navigation