Volume 7, Issue 2 (9-2020)
jhbmi 2020, 7(2): 181-189 |
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HomaieFasih A, Atashi A, Ahsani Tehrani H. Prediction of Breast Cancer Metastasis Using Fuzzy Models based on Data from Iranian Breast Cancer Patients. jhbmi 2020; 7 (2) :181-189
URL: http://jhbmi.ir/article-1-446-en.html
URL: http://jhbmi.ir/article-1-446-en.html
Ph.D. in Medical Informatics, Medical Informatics Research Group, Clinical Research Dept., Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
Abstract: (3141 Views)
Introduction: The metastasis of breast cancer, the spread of cancer to different body parts, is considered as one of the most important factors responsible for the majority of deaths caused by breast cancer in women. Diagnosing the breast cancer metastasis at the earliest stages helps to choose the best treatment and improve the quality of life for patients.
Method: In the present fundamental research, the dataset of Iranian patients available at Breast Cancer Research Center of Motamed Cancer Institute in Tehran was utilized. This study used Mamdani fuzzy inference system, Takagi-Sugeno fuzzy inference system and adaptive neuro-fuzzy inference system (ANFIS) to predict breast cancer metastasis at early stages.
Results: The best prediction error was obtained using adaptive neuro-fuzzy inference system based on fuzzy c-means approach. The opinion of the experts at Breast Cancer Research Center of Motamed Cancer Institute and the prediction error of the assessed model indicated that this prediction system is well-formed.
Conclusion: The optimal proposed prediction system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice
Method: In the present fundamental research, the dataset of Iranian patients available at Breast Cancer Research Center of Motamed Cancer Institute in Tehran was utilized. This study used Mamdani fuzzy inference system, Takagi-Sugeno fuzzy inference system and adaptive neuro-fuzzy inference system (ANFIS) to predict breast cancer metastasis at early stages.
Results: The best prediction error was obtained using adaptive neuro-fuzzy inference system based on fuzzy c-means approach. The opinion of the experts at Breast Cancer Research Center of Motamed Cancer Institute and the prediction error of the assessed model indicated that this prediction system is well-formed.
Conclusion: The optimal proposed prediction system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice
Keywords: Breast Cancer, Metastasis, Mamdani Fuzzy Inference System, Takagi-Sugeno Fuzzy Inference System, Adaptive Neuro-Fuzzy Inference System
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